00611nas a2200145 4500008004100000245012200041210006900163260002700232100001500259700001900274700002000293700001600313700001500329856012100344 2018 eng d00aInstantaneous voltage of electroencephalographic oscillatory activity: An alternative to power and phase measurements0 aInstantaneous voltage of electroencephalographic oscillatory act aSan Diego, CAc11/20181 aAdamek, M.1 aBrunner, Peter1 aMoheimanian, L.1 aScherer, R.1 aSchalk, G. uhttps://www.neurotechcenter.org/publications/2018/instantaneous-voltage-electroencephalographic-oscillatory-activity00574nas a2200133 4500008004100000245012200041210006900163100001500232700001900247700002000266700001600286700001500302856012300317 2018 eng d00aInstantaneous voltage of electroencephalographic oscillatory activity: An alternative to power and phase measurements0 aInstantaneous voltage of electroencephalographic oscillatory act1 aAdamek, M.1 aBrunner, Peter1 aMoheimanian, L.1 aScherer, R.1 aSchalk, G. uhttps://www.neurotechcenter.org/publications/2018/instantaneous-voltage-electroencephalographic-oscillatory-activity-000831nas a2200277 4500008004100000022001400041245009500055210006900150300001600219490000800235653000900243653002500252653002300277653001700300653002300317100001600340700001500356700001400371700001900385700001400404700001400418700001600432700001900448700001500467856007100482 2018 eng d a1388-245700aPassive functional mapping of receptive language areas using electrocorticographic signals0 aPassive functional mapping of receptive language areas using ele a2517 - 25240 v12910aECoG10aElectrocorticography10afunctional mapping10aIntracranial10aReceptive language1 aSwift, J.R.1 aCoon, W.G.1 aGuger, C.1 aBrunner, Peter1 aBunch, M.1 aLynch, T.1 aFrawley, B.1 aRitaccio, A.L.1 aSchalk, G. uhttp://www.sciencedirect.com/science/article/pii/S138824571831228801235nas a2200217 4500008004100000022001400041245012500055210006900180260000800249520053300257100002100790700002000811700002000831700001900851700001900870700001800889700002200907700001600929700002400945856004800969 2017 eng d a1524-462800aContralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors.0 aContralesional BrainComputer Interface Control of a Powered Exos cMay3 aThere are few effective therapies to achieve functional recovery from motor-related disabilities affecting the upper limb after stroke. This feasibility study tested whether a powered exoskeleton driven by a brain-computer interface (BCI), using neural activity from the unaffected cortical hemisphere, could affect motor recovery in chronic hemiparetic stroke survivors. This novel system was designed and configured for a home-based setting to test the feasibility of BCI-driven neurorehabilitation in outpatient environments.1 aBundy, David, T.1 aSouders, Lauren1 aBaranyai, Kelly1 aLeonard, Laura1 aSchalk, Gerwin1 aCoker, Robert1 aMoran, Daniel, W.1 aHuskey, Thy1 aLeuthardt, Eric, C. uhttp://www.ncbi.nlm.nih.gov/pubmed/2855009802569nas a2200241 4500008004100000245013500041210006900176260000800245300001800253490000800271520178000279653006902059100001902128700001702147700001402164700001402178700001802192700002002210700001902230700001502249700001802264856004502282 2017 eng d00aFacephenes and rainbows: Causal evidence for functional and anatomical specificity of face and color processing in the human brain0 aFacephenes and rainbows Causal evidence for functional and anato cNov a12285–122900 v1143 aNeuroscientists have long debated whether some regions of the human brain are exclusively engaged in a single specific mental process. Consistent with this view, fMRI has revealed cortical regions that respond selectively to certain stimulus classes such as faces. However, results from multivoxel pattern analyses (MVPA) challenge this view by demonstrating that category-selective regions often contain information about "nonpreferred" stimulus dimensions. But is this nonpreferred information causally relevant to behavior? Here we report a rare opportunity to test this question in a neurosurgical patient implanted for clinical reasons with strips of electrodes along his fusiform gyri. Broadband gamma electrocorticographic responses in multiple adjacent electrodes showed strong selectivity for faces in a region corresponding to the fusiform face area (FFA), and preferential responses to color in a nearby site, replicating earlier reports. To test the causal role of these regions in the perception of nonpreferred dimensions, we then electrically stimulated individual sites while the patient viewed various objects. When stimulated in the FFA, the patient reported seeing an illusory face (or "facephene"), independent of the object viewed. Similarly, stimulation of color-preferring sites produced illusory "rainbows." Crucially, the patient reported no change in the object viewed, apart from the facephenes and rainbows apparently superimposed on them. The functional and anatomical specificity of these effects indicate that some cortical regions are exclusively causally engaged in a single specific mental process, and prompt caution about the widespread assumption that any information scientists can decode from the brain is causally relevant to behavior.10acortical specificity; electrical stimulation; fusiform face area1 aSchalk, Gerwin1 aKapeller, C.1 aGuger, C.1 aOgawa, H.1 aHiroshima, S.1 aLafer-Sousa, R.1 aSaygin, Z., M.1 aKamada, K.1 aKanwisher, N. uhttp://www.pnas.org/content/114/46/1228502510nas a2200289 4500008004100000022001400041245011800055210006900173260000800242520159500250100002501845700002401870700001701894700002501911700002301936700002101959700002501980700002402005700001902029700001902048700001802067700001902085700002202104700002302126700002302149856004802172 2017 eng d a1091-649000aSpatiotemporal dynamics of word retrieval in speech production revealed by cortical high-frequency band activity.0 aSpatiotemporal dynamics of word retrieval in speech production r cMay3 aWord retrieval is core to language production and relies on complementary processes: the rapid activation of lexical and conceptual representations and word selection, which chooses the correct word among semantically related competitors. Lexical and conceptual activation is measured by semantic priming. In contrast, word selection is indexed by semantic interference and is hampered in semantically homogeneous (HOM) contexts. We examined the spatiotemporal dynamics of these complementary processes in a picture naming task with blocks of semantically heterogeneous (HET) or HOM stimuli. We used electrocorticography data obtained from frontal and temporal cortices, permitting detailed spatiotemporal analysis of word retrieval processes. A semantic interference effect was observed with naming latencies longer in HOM versus HET blocks. Cortical response strength as indexed by high-frequency band (HFB) activity (70-150 Hz) amplitude revealed effects linked to lexical-semantic activation and word selection observed in widespread regions of the cortical mantle. Depending on the subsecond timing and cortical region, HFB indexed semantic interference (i.e., more activity in HOM than HET blocks) or semantic priming effects (i.e., more activity in HET than HOM blocks). These effects overlapped in time and space in the left posterior inferior temporal gyrus and the left prefrontal cortex. The data do not support a modular view of word retrieval in speech production but rather support substantial overlap of lexical-semantic activation and word selection mechanisms in the brain.1 aRiès, Stephanie, K.1 aDhillon, Rummit, K.1 aClarke, Alex1 aKing-Stephens, David1 aLaxer, Kenneth, D.1 aWeber, Peter, B.1 aKuperman, Rachel, A.1 aAuguste, Kurtis, I.1 aBrunner, Peter1 aSchalk, Gerwin1 aLin, Jack, J.1 aParvizi, Josef1 aCrone, Nathan, E.1 aDronkers, Nina, F.1 aKnight, Robert, T. uhttp://www.ncbi.nlm.nih.gov/pubmed/2853340603468nas a2200265 4500008004100000022001400041245013200055210006900187260000800256300001400264490000800278520267200286100001902958700001702977700001902994700001503013700001803028700001903046700001703065700002003082700001903102700001403121700001903135856004803154 2016 eng d a1095-957200aAlpha power indexes task-related networks on large and small scales: A multimodal ECoG study in humans and a non-human primate.0 aAlpha power indexes taskrelated networks on large and small scal cJul a122–1310 v1343 aPerforming different tasks, such as generating motor movements or processing sensory input, requires the recruitment of specific networks of neuronal populations. Previous studies suggested that power variations in the alpha band (8-12Hz) may implement such recruitment of task-specific populations by increasing cortical excitability in task-related areas while inhibiting population-level cortical activity in task-unrelated areas (Klimesch et al., 2007; Jensen and Mazaheri, 2010). However, the precise temporal and spatial relationships between the modulatory function implemented by alpha oscillations and population-level cortical activity remained undefined. Furthermore, while several studies suggested that alpha power indexes task-related populations across large and spatially separated cortical areas, it was largely unclear whether alpha power also differentially indexes smaller networks of task-related neuronal populations. Here we addressed these questions by investigating the temporal and spatial relationships of electrocorticographic (ECoG) power modulations in the alpha band and in the broadband gamma range (70-170Hz, indexing population-level activity) during auditory and motor tasks in five human subjects and one macaque monkey. In line with previous research, our results confirm that broadband gamma power accurately tracks task-related behavior and that alpha power decreases in task-related areas. More importantly, they demonstrate that alpha power suppression lags population-level activity in auditory areas during the auditory task, but precedes it in motor areas during the motor task. This suppression of alpha power in task-related areas was accompanied by an increase in areas not related to the task. In addition, we show for the first time that these differential modulations of alpha power could be observed not only across widely distributed systems (e.g., motor vs. auditory system), but also within the auditory system. Specifically, alpha power was suppressed in the locations within the auditory system that most robustly responded to particular sound stimuli. Altogether, our results provide experimental evidence for a mechanism that preferentially recruits task-related neuronal populations by increasing cortical excitability in task-related cortical areas and decreasing cortical excitability in task-unrelated areas. This mechanism is implemented by variations in alpha power and is common to humans and the non-human primate under study. These results contribute to an increasingly refined understanding of the mechanisms underlying the selection of the specific neuronal populations required for task execution.1 ade Pesters, A.1 aCoon, W., G.1 aBrunner, Peter1 aGunduz, A.1 aRitaccio, A L1 aBrunet, N., M.1 ade Weerd, P.1 aRoberts, M., J.1 aOostenveld, R.1 aFries, P.1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2705796001628nas a2200181 4500008004100000245013100041210006900172260000800241300001200249490000600261520102400267100002401291700002301315700002301338700001801361700001901379856004801398 2016 eng d00aElectrocorticographic mapping of expressive language function without requiring the patient to speak: A report of three cases.0 aElectrocorticographic mapping of expressive language function wi cMar a13–180 v63 aPatients requiring resective brain surgery often undergo functional brain mapping during perioperative planning to localize expressive language areas. Currently, all established protocols to perform such mapping require substantial time and patient participation during verb generation or similar tasks. These issues can make language mapping impractical in certain clinical circumstances (e.g., during awake craniotomies) or with certain populations (e.g., pediatric patients). Thus, it is important to develop new techniques that reduce mapping time and the requirement for active patient participation. Several neuroscientific studies reported that the mere auditory presentation of speech stimuli can engage not only receptive but also expressive language areas. Here, we tested the hypothesis that submission of electrocorticographic (ECoG) recordings during a short speech listening task to an appropriate analysis procedure can identify eloquent expressive language cortex without requiring the patient to speak.1 ade Pesters, Adriana1 aTaplin, AmiLyn, M.1 aAdamo, Matthew, A.1 aRitaccio, A L1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2740880302053nas a2200217 4500008004100000245010300041210006900144260000800213300001200221490000600233520138300239100002301622700002401645700001901669700001701688700002201705700002301727700001801750700001901768856004801787 2016 eng d00aIntraoperative mapping of expressive language cortex using passive real-time electrocorticography.0 aIntraoperative mapping of expressive language cortex using passi cMar a46–510 v53 aIn this case report, we investigated the utility and practicality of passive intraoperative functional mapping of expressive language cortex using high-resolution electrocorticography (ECoG). The patient presented here experienced new-onset seizures caused by a medium-grade tumor in very close proximity to expressive language regions. In preparation of tumor resection, the patient underwent multiple functional language mapping procedures. We examined the relationship of results obtained with intraoperative high-resolution ECoG, extraoperative ECoG utilizing a conventional subdural grid, extraoperative electrical cortical stimulation (ECS) mapping, and functional magnetic resonance imaging (fMRI). Our results demonstrate that intraoperative mapping using high-resolution ECoG is feasible and, within minutes, produces results that are qualitatively concordant to those achieved by extraoperative mapping modalities. They also suggest that functional language mapping of expressive language areas with ECoG may prove useful in many intraoperative conditions given its time efficiency and safety. Finally, they demonstrate that integration of results from multiple functional mapping techniques, both intraoperative and extraoperative, may serve to improve the confidence in or precision of functional localization when pathology encroaches upon eloquent language cortex.1 aTaplin, AmiLyn, M.1 ade Pesters, Adriana1 aBrunner, Peter1 aHermes, Dora1 aDalfino, John, C.1 aAdamo, Matthew, A.1 aRitaccio, A L1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2740880201043nas a2200157 4500008004100000022001400041245014200055210006900197260000800266300001200274490000800286520050700294100001700801700001900818856004800837 2016 eng d a1872-678X00aA method to establish the spatiotemporal evolution of task-related cortical activity from electrocorticographic signals in single trials.0 amethod to establish the spatiotemporal evolution of taskrelated  cSep a76–850 v2713 aProgress in neuroscience depends substantially on the ability to establish the detailed spatial and temporal sequence of neuronal population-level activity across large areas of the brain. Because there is substantial inter-trial variability in neuronal activity, traditional techniques that rely on signal averaging obscure where and when neuronal activity occurs. Thus, up to the present, it has been difficult to examine the detailed progression of neuronal activity across large areas of the brain.1 aCoon, W., G.1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2742730102058nas a2200217 4500008004100000022001400041245006200055210006100117260000800178300001800186490000800204520143200212100002301644700002101667700001901688700002201707700002301729700001901752700002101771856004801792 2016 eng d a1091-649000aNeural correlate of the construction of sentence meaning.0 aNeural correlate of the construction of sentence meaning cOct aE6256–E62620 v1133 aThe neural processes that underlie your ability to read and understand this sentence are unknown. Sentence comprehension occurs very rapidly, and can only be understood at a mechanistic level by discovering the precise sequence of underlying computational and neural events. However, we have no continuous and online neural measure of sentence processing with high spatial and temporal resolution. Here we report just such a measure: intracranial recordings from the surface of the human brain show that neural activity, indexed by $\gamma$-power, increases monotonically over the course of a sentence as people read it. This steady increase in activity is absent when people read and remember nonword-lists, despite the higher cognitive demand entailed, ruling out accounts in terms of generic attention, working memory, and cognitive load. Response increases are lower for sentence structure without meaning (``Jabberwocky'' sentences) and word meaning without sentence structure (word-lists), showing that this effect is not explained by responses to syntax or word meaning alone. Instead, the full effect is found only for sentences, implicating compositional processes of sentence understanding, a striking and unique feature of human language not shared with animal communication systems. This work opens up new avenues for investigating the sequence of neural events that underlie the construction of linguistic meaning.1 aFedorenko, Evelina1 aScott, Terri, L.1 aBrunner, Peter1 aCoon, William, G.1 aPritchett, Brianna1 aSchalk, Gerwin1 aKanwisher, Nancy uhttp://www.ncbi.nlm.nih.gov/pubmed/2767164202115nas a2200205 4500008004100000022001400041245009100055210006900146260000800215300001400223490000800237520151200245100001701757700001501774700001901789700001801808700001601826700001901842856004801861 2016 eng d a1095-957200aOscillatory phase modulates the timing of neuronal activations and resulting behavior.0 aOscillatory phase modulates the timing of neuronal activations a cJun a294–3010 v1333 aHuman behavioral response timing is highly variable from trial to trial. While it is generally understood that behavioral variability must be due to trial-by-trial variations in brain function, it is still largely unknown which physiological mechanisms govern the timing of neural activity as it travels through networks of neuronal populations, and how variations in the timing of neural activity relate to variations in the timing of behavior. In our study, we submitted recordings from the cortical surface to novel analytic techniques to chart the trajectory of neuronal population activity across the human cortex in single trials, and found joint modulation of the timing of this activity and of consequent behavior by neuronal oscillations in the alpha band (8-12Hz). Specifically, we established that the onset of population activity tends to occur during the trough of oscillatory activity, and that deviations from this preferred relationship are related to changes in the timing of population activity and the speed of the resulting behavioral response. These results indicate that neuronal activity incurs variable delays as it propagates across neuronal populations, and that the duration of each delay is a function of the instantaneous phase of oscillatory activity. We conclude that the results presented in this paper are supportive of a general model for variability in the effective speed of information transmission in the human brain and for variability in the timing of human behavior.1 aCoon, W., G.1 aGunduz, A.1 aBrunner, Peter1 aRitaccio, A L1 aPesaran, B.1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2697555101004nas a2200229 4500008004100000022001400041245009000055210006900145260000800214300001400222490000700236520032300243100001800566700002100584700001700605700002200622700002200644700002000666700002100686700001900707856004800726 2016 eng d a1525-506900aProceedings of the Eighth International Workshop on Advances in Electrocorticography.0 aProceedings of the Eighth International Workshop on Advances in  cNov a248–2520 v643 aExcerpted proceedings of the Eighth International Workshop on Advances in Electrocorticography (ECoG), which convened October 15-16, 2015 in Chicago, IL, are presented. The workshop series has become the foremost gathering to present current basic and clinical research in subdural brain signal recording and analysis.1 aRitaccio, A L1 aWilliams, Justin1 aDenison, Tim1 aFoster, Brett, L.1 aStarr, Philip, A.1 aGunduz, Aysegul1 aZijlmans, Maeike1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2778008502409nas a2200217 4500008004100000022001400041245012900055210006900184260000800253300001300261490000700274520171000281100002701991700002502018700002402043700002002067700001902087700001802106700001902124856004802143 2016 eng d a1932-620300aSpatio-Temporal Progression of Cortical Activity Related to Continuous Overt and Covert Speech Production in a Reading Task.0 aSpatioTemporal Progression of Cortical Activity Related to Conti cNov ae01668720 v113 aHow the human brain plans, executes, and monitors continuous and fluent speech has remained largely elusive. For example, previous research has defined the cortical locations most important for different aspects of speech function, but has not yet yielded a definition of the temporal progression of involvement of those locations as speech progresses either overtly or covertly. In this paper, we uncovered the spatio-temporal evolution of neuronal population-level activity related to continuous overt speech, and identified those locations that shared activity characteristics across overt and covert speech. Specifically, we asked subjects to repeat continuous sentences aloud or silently while we recorded electrical signals directly from the surface of the brain (electrocorticography (ECoG)). We then determined the relationship between cortical activity and speech output across different areas of cortex and at sub-second timescales. The results highlight a spatio-temporal progression of cortical involvement in the continuous speech process that initiates utterances in frontal-motor areas and ends with the monitoring of auditory feedback in superior temporal gyrus. Direct comparison of cortical activity related to overt versus covert conditions revealed a common network of brain regions involved in speech that may implement orthographic and phonological processing. Our results provide one of the first characterizations of the spatiotemporal electrophysiological representations of the continuous speech process, and also highlight the common neural substrate of overt and covert speech. These results thereby contribute to a refined understanding of speech functions in the human brain.1 aBrumberg, Jonathan, S.1 aKrusienski, Dean, J.1 aChakrabarti, Shreya1 aGunduz, Aysegul1 aBrunner, Peter1 aRitaccio, A L1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2787559002114nas a2200205 4500008004100000022001400041245020700055210006900262260000800331300001300339490000700352520137500359653002201734100002001756700001901776700001701795700002501812700002301837856004801860 2016 eng d a1553-735800aSpontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change.0 aSpontaneous Decoding of the Timing and Content of Human Object P cJan ae10046600 v123 aThe link between object perception and neural activity in visual cortical areas is a problem of fundamental importance in neuroscience. Here we show that electrical potentials from the ventral temporal cortical surface in humans contain sufficient information for spontaneous and near-instantaneous identification of a subject's perceptual state. Electrocorticographic (ECoG) arrays were placed on the subtemporal cortical surface of seven epilepsy patients. Grayscale images of faces and houses were displayed rapidly in random sequence. We developed a template projection approach to decode the continuous ECoG data stream spontaneously, predicting the occurrence, timing and type of visual stimulus. In this setting, we evaluated the independent and joint use of two well-studied features of brain signals, broadband changes in the frequency power spectrum of the potential and deflections in the raw potential trace (event-related potential; ERP). Our ability to predict both the timing of stimulus onset and the type of image was best when we used a combination of both the broadband response and ERP, suggesting that they capture different and complementary aspects of the subject's perceptual state. Specifically, we were able to predict the timing and type of 96% of all stimuli, with less than 5% false positive rate and a {\textasciitilde}20ms error in timing.10aVisual Perception1 aMiller, Kai, J.1 aSchalk, Gerwin1 aHermes, Dora1 aOjemann, Jeffrey, G.1 aRao, Rajesh, P. N. uhttp://www.ncbi.nlm.nih.gov/pubmed/2682089902154nas a2200217 4500008004100000022001400041245008300055210006900138260000800207300001000215490000600225520150300231100002301734700001901757700002101776700002701797700001901824700002301843700002201866856004801888 2016 eng d a2045-232200aWord pair classification during imagined speech using direct brain recordings.0 aWord pair classification during imagined speech using direct bra cMay a258030 v63 aPeople that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70-150þinspaceHz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (meanþinspace=þinspace58%; pþinspace<þinspace0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (meanþinspace=þinspace89% and 86%, respectively; pþinspace<þinspace0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications.1 aMartin, Stéphanie1 aBrunner, Peter1 aIturrate, Iñaki1 aMillán, José, Del R.1 aSchalk, Gerwin1 aKnight, Robert, T.1 aPasley, Brian, N. uhttp://www.ncbi.nlm.nih.gov/pubmed/2716545202272nas a2200277 4500008004100000245008600041210006900127260001200196520140600208653003301614653002901647653002001676653000901696653002501705653002401730653002001754653002201774100001401796700001401810700002401824700001501848700001901863700001901882700001601901856007701917 2015 eng d00aBrain-to-text: Decoding spoken sentences from phone representations in the brain.0 aBraintotext Decoding spoken sentences from phone representations c06/20153 aIt has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. However, until now it remained an unsolved challenge to decode continuously spoken speech from the neural substrate associated with speech and language processing. Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG) recordings.Specifically, we implemented a system, which we call Brain-To-Text that models single phones, employs techniques from automatic speech recognition (ASR), and thereby transforms brain activity while speaking into the corresponding textual representation. Our results demonstrate that our system can achieve word error rates as low as 25% and phone error rates below 50%. Additionally, our approach contributes to the current understanding of the neural basis of continuous speech production by identifying those cortical regions that hold substantial information about individual phones. In conclusion, the Brain-To- Text system described in this paper represents an important step toward human-machine communication based on imagined speech.10aautomatic speech recognition10abrain-computer interface10abroadband gamma10aECoG10aElectrocorticography10apattern recognition10aspeech decoding10aspeech production1 aHerff, C.1 aHeger, D.1 ade Pesters, Adriana1 aTelaar, D.1 aBrunner, Peter1 aSchalk, Gerwin1 aSchultz, T. uhttp://journal.frontiersin.org/article/10.3389/fnins.2015.00217/abstract02231nas a2200205 4500008004100000245007600041210006900117260001200186520158000198653001401778653000801792653001001800653002201810653003101832100001601863700002501879700002501904700001901929856007701948 2015 eng d00aCortical alpha activity predicts the confidence in an impending action.0 aCortical alpha activity predicts the confidence in an impending  c07/20153 aWhen we make a decision, we experience a degree of confidence that our choice may lead to a desirable outcome. Recent studies in animals have probed the subjective aspects of the choice confidence using confidence-reporting tasks. These studies showed that estimates of the choice confidence substantially modulate neural activity in multiple regions of the brain. Building on these findings, we investigated the neural representation of the confidence in a choice in humans who explicitly reported the confidence in their choice. Subjects performed a perceptual decision task in which they decided between choosing a button press or a saccade while we recorded EEG activity. Following each choice, subjects indicated whether they were sure or unsure about the choice. We found that alpha activity strongly encodes a subject's confidence level in a forthcoming button press choice. The neural effect of the subjects' confidence was independent of the reaction time and independent of the sensory input modeled as a decision variable. Furthermore, the effect is not due to a general cognitive state, such as reward expectation, because the effect was specifically observed during button press choices and not during saccade choices. The neural effect of the confidence in the ensuing button press choice was strong enough that we could predict, from independent single trial neural signals, whether a subject was going to be sure or unsure of an ensuing button press choice. In sum, alpha activity in human cortex provides a window into the commitment to make a hand movement.10acertainty10aEEG10ahuman10aneural correlates10aperceptual decision-making1 aKubánek, J1 aHill, Jeremy, Jeremy1 aSnyder, Lawrence, H.1 aSchalk, Gerwin uhttp://journal.frontiersin.org/article/10.3389/fnins.2015.00243/abstract01280nas a2200205 4500008004100000022001400041245008500055210006900140260000800209300001100217490000700228520068900235653001600924100001200940700001700952700001900969700001900988700001901007856004801026 2015 eng d a1741-255200aThe effects of spatial filtering and artifacts on electrocorticographic signals.0 aeffects of spatial filtering and artifacts on electrocorticograp cOct a0560080 v123 aElectrocorticographic (ECoG) signals contain noise that is common to all channels and noise that is specific to individual channels. Most published ECoG studies use common average reference (CAR) spatial filters to remove common noise, but CAR filters may introduce channel-specific noise into other channels. To address this concern, scientists often remove artifactual channels prior to data analysis. However, removing these channels depends on expert-based labeling and may also discard useful data. Thus, the effects of spatial filtering and artifacts on ECoG signals have been largely unknown. This study aims to quantify these effects and thereby address this gap in knowledge.10aYoung Adult1 aLiu, Y.1 aCoon, W., G.1 ade Pesters, A.1 aBrunner, Peter1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2626844602354nas a2200277 4500008004100000022001400041245008600055210006900141260001200210300000700222490000600229520153700235653003201772653002701804653002601831653002201857653001201879100001801891700002601909700001901935700002001954700001801974700001701992700001902009856004802028 2015 eng d a1662-516100aElectrocorticographic representations of segmental features in continuous speech.0 aElectrocorticographic representations of segmental features in c c02/2015 a970 v93 aAcoustic speech output results from coordinated articulation of dozens of muscles, bones and cartilages of the vocal mechanism. While we commonly take the fluency and speed of our speech productions for granted, the neural mechanisms facilitating the requisite muscular control are not completely understood. Previous neuroimaging and electrophysiology studies of speech sensorimotor control has typically concentrated on speech sounds (i.e., phonemes, syllables and words) in isolation; sentence-length investigations have largely been used to inform coincident linguistic processing. In this study, we examined the neural representations of segmental features (place and manner of articulation, and voicing status) in the context of fluent, continuous speech production. We used recordings from the cortical surface [electrocorticography (ECoG)] to simultaneously evaluate the spatial topography and temporal dynamics of the neural correlates of speech articulation that may mediate the generation of hypothesized gestural or articulatory scores. We found that the representation of place of articulation involved broad networks of brain regions during all phases of speech production: preparation, execution and monitoring. In contrast, manner of articulation and voicing status were dominated by auditory cortical responses after speech had been initiated. These results provide a new insight into the articulatory and auditory processes underlying speech production in terms of their motor requirements and acoustic correlates.10aelectrocorticography (ECoG)10amanner of articulation10aplace of articulation10aspeech processing10avoicing1 aLotte, Fabien1 aBrumberg, Jonathan, S1 aBrunner, Peter1 aGunduz, Aysegul1 aRitaccio, A L1 aGuan, Cuntai1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2575964701790nas a2200181 4500008004100000245011400041210006900155260001200224490000600236520114100242653003601383653002501419653002401444653001701468653002701485100001901512856007701531 2015 eng d00aA general framework for dynamic cortical function: the function-through-biased-oscillations (FBO) hypothesis.0 ageneral framework for dynamic cortical function the functionthro c06/20150 v93 aA central goal of neuroscience is to determine how the brain’s relatively static anatomy can support dynamic cortical function, i.e., cortical function that varies according to task demands. In pursuit of this goal, scientists have produced a large number of experimental results and established influential conceptual frameworks, in particular communication-through-coherence (CTC) and gating-by-inhibition (GBI), but these data and frameworks have not provided a parsimonious view of the principles that underlie cortical function. Here I synthesize these existing experimental results and the CTC and GBI frameworks, and propose the function-through-biased-oscillations (FBO) hypothesis as a model to understand dynamic cortical function. The FBO hypothesis suggests that oscillatory voltage amplitude is the principal measurement that directly reflects cortical excitability, that asymmetries in voltage amplitude explain a range of brain signal phenomena, and that predictive variations in such asymmetric oscillations provide a simple and general model for information routing that can help to explain dynamic cortical function.10acommunication-through-coherence10agating-by-inhibition10ainformation routing10aoscillations10aoscillatory modulation1 aSchalk, Gerwin uhttp://journal.frontiersin.org/article/10.3389/fnhum.2015.00352/abstract02118nas a2200217 4500008004100000245008100041210006900122520141400191653002301605653003501628653001901663653003201682100001701714700001901731700002001750700001501770700001801785700002001803700001901823856005801842 2015 eng d00aIdentifying the Attended Speaker Using Electrocorticographic (ECoG) Signals.0 aIdentifying the Attended Speaker Using Electrocorticographic ECo3 aPeople affected by severe neuro-degenerative diseases (e.g., late-stage amyotrophic lateral sclerosis (ALS) or locked-in syndrome) eventually lose all muscular control. Thus, they cannot use traditional assistive communication devices that depend on muscle control, or brain-computer interfaces (BCIs) that depend on the ability to control gaze. While auditory and tactile BCIs can provide communication to such individuals, their use typically entails an artificial mapping between the stimulus and the communication intent. This makes these BCIs difficult to learn and use. In this study, we investigated the use of selective auditory attention to natural speech as an avenue for BCI communication. In this approach, the user communicates by directing his/her attention to one of two simultaneously presented speakers. We used electrocorticographic (ECoG) signals in the gamma band (70–170 Hz) to infer the identity of attended speaker, thereby removing the need to learn such an artificial mapping. Our results from twelve human subjects show that a single cortical location over superior temporal gyrus or pre-motor cortex is typically sufficient to identify the attended speaker within 10 s and with 77% accuracy (50% accuracy due to chance). These results lay the groundwork for future studies that may determine the real-time performance of BCIs based on selective auditory attention to speech.10aauditory attention10aBrain-computer interface (BCI)10aCocktail Party10aelectrocorticography (ECoG)1 aDijkstra, K.1 aBrunner, Peter1 aGunduz, Aysegul1 aCoon, W.G.1 aRitaccio, A L1 aFarquhar, Jason1 aSchalk, Gerwin uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4776341/02134nas a2200253 4500008004100000020002200041022002200063245009200085210006900177260005700246300001200303520128900315653002001604653002501624653002801649653002001677653001701697100001901714700001901733700001701752700002401769700002101793856006601814 2015 eng d a978-3-319-25188-2 a978-3-319-25190-500aNear-Instantaneous Classification of Perceptual States from Cortical Surface Recordings0 aNearInstantaneous Classification of Perceptual States from Corti aNew York City, NYbSpringer International Publishing a105-1143 aHuman visual processing is of such complexity that, despite decades of focused research, many basic questions remain unanswered. Although we know that the inferotemporal cortex is a key region in object recognition, we don’t fully understand its physiologic role in brain function, nor do we have the full set of tools to explore this question. Here we show that electrical potentials from the surface of the human brain contain enough information to decode a subject’s perceptual state accurately, and with fine temporal precision. Electrocorticographic (ECoG) arrays were placed over the inferotemporal cortical areas of seven subjects. Pictures of faces and houses were quickly presented while each subject performed a simple visual task. Results showed that two well-known types of brain signals—event-averaged broadband power and event-averaged raw potential—can independently or together be used to classify the presented image. When applied to continuously recorded brain activity, our decoding technique could accurately predict whether each stimulus was a face, house, or neither, with  20 ms timing error. These results provide a roadmap for improved brain-computer interfacing tools to help neurosurgeons, research scientists, engineers, and, ultimately, patients.10abroadband power10aElectrocorticography10aevent-related potential10afusiform cortex10ahuman vision1 aMiller, Kai, J1 aSchalk, Gerwin1 aHermes, Dora1 aOjemann, Jeffrey, G1 aRao, Rajesh, P N uhttp://link.springer.com/chapter/10.1007/978-3-319-25190-5_1002073nas a2200241 4500008004100000022001400041245011100055210006900166260001200235300001100247490000700258520141600265653001001681653000801691653000901699653000801708653001201716653001101728653000801739100001701747700001901764856004801783 2015 eng d a1559-008900aNeuralAct: A Tool to Visualize Electrocortical (ECoG) Activity on a Three-Dimensional Model of the Cortex.0 aNeuralAct A Tool to Visualize Electrocortical ECoG Activity on a c04/2015 a167-740 v133 a

Electrocorticography (ECoG) records neural signals directly from the surface of the cortex. Due to its high temporal and favorable spatial resolution, ECoG has emerged as a valuable new tool in acquiring cortical activity in cognitive and systems neuroscience. Many studies using ECoG visualized topographies of cortical activity or statistical tests on a three-dimensional model of the cortex, but a dedicated tool for this function has not yet been described. In this paper, we describe the NeuralAct package that serves this purpose. This package takes as input the 3D coordinates of the recording sensors, a cortical model in the same coordinate system (e.g., Talairach), and the activation data to be visualized at each sensor. It then aligns the sensor coordinates with the cortical model, convolves the activation data with a spatial kernel, and renders the resulting activations in color on the cortical model. The NeuralAct package can plot cortical activations of an individual subject as well as activations averaged over subjects. It is capable to render single images as well as sequences of images. The software runs under Matlab and is stable and robust. We here provide the tool and describe its visualization capabilities and procedures. The provided package contains thoroughly documented code and includes a simple demo that guides the researcher through the functionality of the tool.

10aBrain10aDOT10aECoG10aEEG10aimaging10aMatlab10aMEG1 aKubanek, Jan1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2538164101282nas a2200181 4500008004100000022001400041245005500055210004900110260001200159300001200171520073200183653003500915100002000950700002200970700001900992700001801011856007101029 2015 eng d a0018-921900aThe Plurality of Human Brain-Computer Interfacing.0 aPlurality of Human BrainComputer Interfacing c06/2015 a868-8703 aThe articles in this special issue focus on brain-computer interfacing. The papers are dedicated to this growing and diversifying research enterprise, and features important review articles as well as some important current examples of research in this area. The field of brain-computer interface (BCI) research began to develop about 25 years ago and transformed from initially isolated demonstrations by a few groups into a large scientific enterprise that is currently producing hundreds of peer-reviewed articles and several dedicated conferences and workshops each year. This level of productivity is reflective of the large and continually growing enthusiasm by the scientific community, funding agencies, and the public.10aBrain-computer interface (BCI)1 aMueller-Putz, G1 aMillán, José, R1 aSchalk, Gerwin1 aMueller, K.R. uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=711530201375nas a2200277 4500008004100000022001400041245009100055210006900146260000800215300001400223490000700237520056000244653001100804100001800815700002000833700002000853700002100873700002300894700001900917700002700936700002500963700002100988700002101009700001901030856004801049 2015 eng d a1525-506900aProceedings of the Seventh International Workshop on Advances in Electrocorticography.0 aProceedings of the Seventh International Workshop on Advances in cOct a312–3200 v513 aThe Seventh International Workshop on Advances in Electrocorticography (ECoG) convened in Washington, DC, on November 13-14, 2014. Electrocorticography-based research continues to proliferate widely across basic science and clinical disciplines. The 2014 workshop highlighted advances in neurolinguistics, brain-computer interface, functional mapping, and seizure termination facilitated by advances in the recording and analysis of the ECoG signal. The following proceedings document summarizes the content of this successful multidisciplinary gathering.10aHumans1 aRitaccio, A L1 aMatsumoto, Riki1 aMorrell, Martha1 aKamada, Kyousuke1 aKoubeissi, Mohamad1 aPoeppel, David1 aLachaux, Jean-Philippe1 aYanagisawa, Yakufumi1 aHirata, Masayuki1 aGuger, Christoph1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2632259401581nas a2200205 4500008004100000020002200041022002200063245003800085210003800123260005700161300001000218520096900228100001901197700001701216700001501233700002301248700001801271700001901289856006701308 2015 eng d a978-3-319-25188-2 a978-3-319-25190-500aTowards an Auditory Attention BCI0 aTowards an Auditory Attention BCI aNew York City, NYbSpringer International Publishing a29-423 aPeople affected by severe neuro-degenerative diseases (e.g., late-stage amyotrophic lateral sclerosis (ALS) or locked-in syndrome) eventually lose all muscular control and are no longer able to gesture or speak. For this population, an auditory BCI is one of only a few remaining means of communication. All currently used auditory BCIs require a relatively artificial mapping between a stimulus and a communication output. This mapping is cumbersome to learn and use. Recent studies suggest that electrocorticographic (ECoG) signals in the gamma band (i.e., 70–170 Hz) can be used to infer the identity of auditory speech stimuli, effectively removing the need to learn such an artificial mapping. However, BCI systems that use this physiological mechanism for communication purposes have not yet been described. In this study, we explore this possibility by implementing a BCI2000-based real-time system that uses ECoG signals to identify the attended speaker.1 aBrunner, Peter1 aDijkstra, K.1 aCoon, W.G.1 aMellinger, Jürgen1 aRitaccio, A L1 aSchalk, Gerwin uhttp://link.springer.com/chapter/10.1007%2F978-3-319-25190-5_402627nas a2200277 4500008004100000245009500041210006900136260001200205490000600217520181900223653002602042653001402068653000902082653000802091653002802099653000802127100002202135700002002157700001702177700001902194700001902213700002602232700002302258700002002281856004802301 2014 eng d00aAssessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.0 aAssessing dynamics spatial scale and uncertainty in taskrelated  c03/20140 v83 aThe brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.10acanonical correlation10acoherence10aECoG10aEEG10afunctional connectivity10aMEG1 aStephen, Emily, P1 aLepage, Kyle, Q1 aEden, Uri, T1 aBrunner, Peter1 aSchalk, Gerwin1 aBrumberg, Jonathan, S1 aGuenther, Frank, H1 aKramer, Mark, A uhttp://www.ncbi.nlm.nih.gov/pubmed/2467829502900nas a2200277 4500008004100000245008800041210006900129260001200198490000600210520205900216653001802275653001902293653002502312653002402337653002202361100002302383700001902406700002002425700002402445700002202469700001902491700001902510700002302529700002202552856004802574 2014 eng d00aDecoding spectrotemporal features of overt and covert speech from the human cortex.0 aDecoding spectrotemporal features of overt and covert speech fro c03/20140 v73 aAuditory perception and auditory imagery have been shown to activate overlapping brain regions. We hypothesized that these phenomena also share a common underlying neural representation. To assess this, we used electrocorticography intracranial recordings from epileptic patients performing an out loud or a silent reading task. In these tasks, short stories scrolled across a video screen in two conditions: subjects read the same stories both aloud (overt) and silently (covert). In a control condition the subject remained in a resting state. We first built a high gamma (70–150 Hz) neural decoding model to reconstruct spectrotemporal auditory features of self-generated overt speech. We then evaluated whether this same model could reconstruct auditory speech features in the covert speech condition. Two speech models were tested: a spectrogram and a modulation-based feature space. For the overt condition, reconstruction accuracy was evaluated as the correlation between original and predicted speech features, and was significant in each subject (p < 0.00001; paired two-sample t-test). For the covert speech condition, dynamic time warping was first used to realign the covert speech reconstruction with the corresponding original speech from the overt condition. Reconstruction accuracy was then evaluated as the correlation between original and reconstructed speech features. Covert reconstruction accuracy was compared to the accuracy obtained from reconstructions in the baseline control condition. Reconstruction accuracy for the covert condition was significantly better than for the control condition (p < 0.005; paired two-sample t-test). The superior temporal gyrus, pre- and post-central gyrus provided the highest reconstruction information. The relationship between overt and covert speech reconstruction depended on anatomy. These results provide evidence that auditory representations of covert speech can be reconstructed from models that are built from an overt speech data set, supporting a partially shared neural substrate.10acovert speech10adecoding model10aElectrocorticography10apattern recognition10aspeech production1 aMartin, Stéphanie1 aBrunner, Peter1 aHoldgraf, Chris1 aHeinze, Hans-Jochen1 aCrone, Nathan, E.1 aRieger, Jochem1 aSchalk, Gerwin1 aKnight, Robert, T.1 aPasley, Brian, N. uhttp://www.ncbi.nlm.nih.gov/pubmed/2490440402771nas a2200253 4500008004100000022001400041245014300055210006900198260001200267300000800279490000600287520196800293653002202261653003202283653001502315653002102330653001802351100001702369700002402386700002102410700001902431700001902450856004802469 2014 eng d a1662-516100aECoG high gamma activity reveals distinct cortical representations of lyrics passages, harmonic and timbre-related changes in a rock song.0 aECoG high gamma activity reveals distinct cortical representatio c10/2014 a7980 v83 a

Listening to music moves our minds and moods, stirring interest in its neural underpinnings. A multitude of compositional features drives the appeal of natural music. How such original music, where a composer's opus is not manipulated for experimental purposes, engages a listener's brain has not been studied until recently. Here, we report an in-depth analysis of two electrocorticographic (ECoG) data sets obtained over the left hemisphere in ten patients during presentation of either a rock song or a read-out narrative. First, the time courses of five acoustic features (intensity, presence/absence of vocals with lyrics, spectral centroid, harmonic change, and pulse clarity) were extracted from the audio tracks and found to be correlated with each other to varying degrees. In a second step, we uncovered the specific impact of each musical feature on ECoG high-gamma power (70-170 Hz) by calculating partial correlations to remove the influence of the other four features. In the music condition, the onset and offset of vocal lyrics in ongoing instrumental music was consistently identified within the group as the dominant driver for ECoG high-gamma power changes over temporal auditory areas, while concurrently subject-individual activation spots were identified for sound intensity, timbral, and harmonic features. The distinct cortical activations to vocal speech-related content embedded in instrumental music directly demonstrate that song integrated in instrumental music represents a distinct dimension in complex music. In contrast, in the speech condition, the full sound envelope was reflected in the high gamma response rather than the onset or offset of the vocal lyrics. This demonstrates how the contributions of stimulus features that modulate the brain response differ across the two examples of a full-length natural stimulus, which suggests a context-dependent feature selection in the processing of complex auditory stimuli.

10aacoustic features10aelectrocorticography (ECoG)10ahigh gamma10amusic processing10anatural music1 aSturm, Irene1 aBlankertz, Benjamin1 aPotes, Cristhian1 aSchalk, Gerwin1 aCurio, Gabriel uhttp://www.ncbi.nlm.nih.gov/pubmed/2535279902766nas a2200205 4500008004100000245009500041210007100136260001200207490000700219520209000226653002902316653002102345653003002366653002102396653002702417100002502444700002402469700001902493856004802512 2014 eng d00aA general method for assessing brain–computer interface performance and its limitations.0 ageneral method for assessing brain–computer interface performanc c03/20140 v113 aObjective. When researchers evaluate brain–computer interface (BCI) systems, we want quantitative answers to questions such as: How good is the system's performance? How good does it need to be? and: Is it capable of reaching the desired level in future? In response to the current lack of objective, quantitative, study-independent approaches, we introduce methods that help to address such questions. We identified three challenges: (I) the need for efficient measurement techniques that adapt rapidly and reliably to capture a wide range of performance levels; (II) the need to express results in a way that allows comparison between similar but non-identical tasks; (III) the need to measure the extent to which certain components of a BCI system (e.g. the signal processing pipeline) not only support BCI performance, but also potentially restrict the maximum level it can reach. Approach. For challenge (I), we developed an automatic staircase method that adjusted task difficulty adaptively along a single abstract axis. For challenge (II), we used the rate of information gain between two Bernoulli distributions: one reflecting the observed success rate, the other reflecting chance performance estimated by a matched random-walk method. This measure includes Wolpaw's information transfer rate as a special case, but addresses the latter's limitations including its restriction to item-selection tasks. To validate our approach and address challenge (III), we compared four healthy subjects' performance using an EEG-based BCI, a 'Direct Controller' (a high-performance hardware input device), and a 'Pseudo-BCI Controller' (the same input device, but with control signals processed by the BCI signal processing pipeline). Main results. Our results confirm the repeatability and validity of our measures, and indicate that our BCI signal processing pipeline reduced attainable performance by about 33% (21 bits/min). Significance. Our approach provides a flexible basis for evaluating BCI performance and its limitations, across a wide range of tasks and task difficulties.10abrain-computer interface10ainformation gain10ainformation transfer rate10aNeuroprosthetics10aperformance evaluation1 aHill, Jeremy, Jeremy1 aHäuser, Ann-Katrin1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2465840602937nas a2200253 4500008004100000022001400041245009000055210006900145260001200214300001000226490000600236520216200242653002402404653003202428653002702460653001402487653003302501100001702534700002502551700002202576700001802598700001902616856004802635 2014 eng d a2213-158200aLocalizing ECoG electrodes on the cortical anatomy without post-implantation imaging.0 aLocalizing ECoG electrodes on the cortical anatomy without posti c08/2014 a64-760 v63 a

INTRODUCTION: Electrocorticographic (ECoG) grids are placed subdurally on the cortex in people undergoing cortical resection to delineate eloquent cortex. ECoG signals have high spatial and temporal resolution and thus can be valuable for neuroscientific research. The value of these data is highest when they can be related to the cortical anatomy. Existing methods that establish this relationship rely either on post-implantation imaging using computed tomography (CT), magnetic resonance imaging (MRI) or X-Rays, or on intra-operative photographs. For research purposes, it is desirable to localize ECoG electrodes on the brain anatomy even when post-operative imaging is not available or when intra-operative photographs do not readily identify anatomical landmarks.

METHODS: We developed a method to co-register ECoG electrodes to the underlying cortical anatomy using only a pre-operative MRI, a clinical neuronavigation device (such as BrainLab VectorVision), and fiducial markers. To validate our technique, we compared our results to data collected from six subjects who also had post-grid implantation imaging available. We compared the electrode coordinates obtained by our fiducial-based method to those obtained using existing methods, which are based on co-registering pre- and post-grid implantation images.

RESULTS: Our fiducial-based method agreed with the MRI-CT method to within an average of 8.24 mm (mean, median = 7.10 mm) across 6 subjects in 3 dimensions. It showed an average discrepancy of 2.7 mm when compared to the results of the intra-operative photograph method in a 2D coordinate system. As this method does not require post-operative imaging such as CTs, our technique should prove useful for research in intra-operative single-stage surgery scenarios. To demonstrate the use of our method, we applied our method during real-time mapping of eloquent cortex during a single-stage surgery. The results demonstrated that our method can be applied intra-operatively in the absence of post-operative imaging to acquire ECoG signals that can be valuable for neuroscientific investigations.

10aauditory processing10aelectrocorticography (ECoG)10aelectrode localization10afiducials10ainteraoperative localization1 aGupta, Disha1 aHill, Jeremy, Jeremy1 aAdamo, Matthew, A1 aRitaccio, A L1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2537941701857nas a2200421 4500008004100000022001400041245008900055210006900144260001200213300001100225490000700236520059500243653001800838653002900856653003500885653002500920653002300945653004300968653003201011653002101043653002201064653001801086100001801104700001901122700002001141700001701161700002401178700001901202700002101221700002001242700002301262700002201285700001601307700002401323700002101347700001901368856004801387 2014 eng d a1525-506900aProceedings of the Fifth International Workshop on Advances in Electrocorticography.0 aProceedings of the Fifth International Workshop on Advances in E c12/2014 a183-920 v413 a

The Fifth International Workshop on Advances in Electrocorticography convened in San Diego, CA, on November 7-8, 2013. Advancements in methodology, implementation, and commercialization across both research and in the interval year since the last workshop were the focus of the gathering. Electrocorticography (ECoG) is now firmly established as a preferred signal source for advanced research in functional, cognitive, and neuroprosthetic domains. Published output in ECoG fields has increased tenfold in the past decade. These proceedings attempt to summarize the state of the art.

10aBrain Mapping10abrain-computer interface10aelectrical stimulation mapping10aElectrocorticography10afunctional mapping10aGamma-frequency electroencephalography10aHigh-frequency oscillations10aNeuroprosthetics10aSeizure detection10aSubdural grid1 aRitaccio, A L1 aBrunner, Peter1 aGunduz, Aysegul1 aHermes, Dora1 aHirsch, Lawrence, J1 aJacobs, Joshua1 aKamada, Kyousuke1 aKastner, Sabine1 aKnight, Robert, T.1 aLesser, Ronald, P1 aMiller, Kai1 aSejnowski, Terrence1 aWorrell, Gregory1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2546121302881nas a2200397 4500008004100000022001400041245011100055210006900166260001200235300001100247490000700258520172500265653001501990653002002005653001802025653002002043653002502063653002702088653002402115653001102139653001102150653001302161653002902174653003202203653001102235100002602246700001902272700002602291700002402317700001902341700001902360700001802379700002302397700001502420856004802435 2014 eng d a1933-071500aReal-time functional mapping: potential tool for improving language outcome in pediatric epilepsy surgery.0 aRealtime functional mapping potential tool for improving languag c09/2014 a287-950 v143 a

Accurate language localization expands surgical treatment options for epilepsy patients and reduces the risk of postsurgery language deficits. Electrical cortical stimulation mapping (ESM) is considered to be the clinical gold standard for language localization. While ESM affords clinically valuable results, it can be poorly tolerated by children, requires active participation and compliance, carries a risk of inducing seizures, is highly time consuming, and is labor intensive. Given these limitations, alternative and/or complementary functional localization methods such as analysis of electrocorticographic (ECoG) activity in high gamma frequency band in real time are needed to precisely identify eloquent cortex in children. In this case report, the authors examined 1) the use of real-time functional mapping (RTFM) for language localization in a high gamma frequency band derived from ECoG to guide surgery in an epileptic pediatric patient and 2) the relationship of RTFM mapping results to postsurgical language outcomes. The authors found that RTFM demonstrated relatively high sensitivity (75%) and high specificity (90%) when compared with ESM in a "next-neighbor" analysis. While overlapping with ESM in the superior temporal region, RTFM showed a few other areas of activation related to expressive language function, areas that were eventually resected during the surgery. The authors speculate that this resection may be associated with observed postsurgical expressive language deficits. With additional validation in more subjects, this finding would suggest that surgical planning and associated assessment of the risk/benefit ratio would benefit from information provided by RTFM mapping.

10aAdolescent10aAnticonvulsants10aBrain Mapping10aCerebral Cortex10aElectric Stimulation10aElectroencephalography10aEpilepsies, Partial10aFemale10aHumans10aLanguage10aNeuropsychological Tests10aSensitivity and Specificity10aSpeech1 aKorostenskaja, Milena1 aChen, Po-Ching1 aSalinas, Christine, M1 aWesterveld, Michael1 aBrunner, Peter1 aSchalk, Gerwin1 aCook, Jane, C1 aBaumgartner, James1 aLee, Ki, H uhttp://www.ncbi.nlm.nih.gov/pubmed/2499581502800nas a2200349 4500008004100000245012100041210006900162260001200231520168800243653003501931653002501966653003201991653002102023653004902044653002302093653001502116653001302131100002602144700002002170700002102190700001902211700001902230700001702249700002602266700002102292700002002313700001802333700001902351700001702370700001502387856004802402 2014 eng d00aReal-Time Functional Mapping with Electrocorticography in Pediatric Epilepsy: Comparison with fMRI and ESM Findings.0 aRealTime Functional Mapping with Electrocorticography in Pediatr c07/20143 aSIGFRIED (SIGnal modeling For Real-time Identification and Event Detection) software provides real-time functional mapping (RTFM) of eloquent cortex for epilepsy patients preparing to undergo resective surgery. This study presents the first application of paradigms used in functional magnetic resonance (fMRI) and electrical cortical stimulation mapping (ESM) studies for shared functional cortical mapping in the context of RTFM. Results from the 3 modalities are compared. A left-handed 13-year-old male with intractable epilepsy participated in functional mapping for localization of eloquent language cortex with fMRI, ESM, and RTFM. For RTFM, data were acquired over the frontal and temporal cortex. Several paradigms were sequentially presented: passive (listening to stories) and active (picture naming and verb generation). For verb generation and story processing, fMRI showed atypical right lateralizing language activation within temporal lobe regions of interest and bilateral frontal activation with slight right lateralization. Left hemisphere ESM demonstrated no eloquent language areas. RTFM procedures using story processing and picture naming elicited activity in the right lateral and basal temporal regions. Verb generation elicited strong right lateral temporal lobe activation, as well as left frontal lobe activation. RTFM results confirmed atypical language lateralization evident from fMRI and ESM. We demonstrated the feasibility and usefulness of a new RTFM stimulation paradigm during presurgical evaluation. Block design paradigms used in fMRI may be optimal for this purpose. Further development is needed to create age-appropriate RTFM test batteries.10aBrain-computer interface (BCI)10acortical stimulation10aelectrocorticography (ECoG)10aepilepsy surgery10afunctional magnetic resonance imaging (fMRI)10afunctional mapping10apediatrics10aSIGFRIED1 aKorostenskaja, Milena1 aWilson, Adam, J1 aRose, Douglas, F1 aBrunner, Peter1 aSchalk, Gerwin1 aLeach, James1 aMangano, Francesco, T1 aFujiwara, Hisako1 aRozhkov, Leonid1 aHarris, Elana1 aChen, Po-Ching1 aSeo, Joo-Hee1 aLee, Ki, H uhttp://www.ncbi.nlm.nih.gov/pubmed/2429316102940nas a2200229 4500008004100000245006800041210006600109260001200175520223700187653002402424653002502448653002302473653002202496653002602518100001702544700002502561700001902586700002002605700001802625700001902643856004802662 2014 eng d00aSimultaneous Real-Time Monitoring of Multiple Cortical Systems.0 aSimultaneous RealTime Monitoring of Multiple Cortical Systems c10/20143 aOBJECTIVE: Real-time monitoring of the brain is potentially valuable for performance monitoring, communication, training or rehabilitation. In natural situations, the brain performs a complex mix of various sensory, motor or cognitive functions. Thus, real-time brain monitoring would be most valuable if (a) it could decode information from multiple brain systems simultaneously, and (b) this decoding of each brain system were robust to variations in the activity of other (unrelated) brain systems. Previous studies showed that it is possible to decode some information from different brain systems in retrospect and/or in isolation. In our study, we set out to determine whether it is possible to simultaneously decode important information about a user from different brain systems in real time, and to evaluate the impact of concurrent activity in different brain systems on decoding performance. APPROACH: We study these questions using electrocorticographic signals recorded in humans. We first document procedures for generating stable decoding models given little training data, and then report their use for offline and for real-time decoding from 12 subjects (six for offline parameter optimization, six for online experimentation). The subjects engage in tasks that involve movement intention, movement execution and auditory functions, separately, and then simultaneously. Main Results: Our real-time results demonstrate that our system can identify intention and movement periods in single trials with an accuracy of 80.4% and 86.8%, respectively (where 50% would be expected by chance). Simultaneously, the decoding of the power envelope of an auditory stimulus resulted in an average correlation coefficient of 0.37 between the actual and decoded power envelopes. These decoders were trained separately and executed simultaneously in real time. SIGNIFICANCE: This study yielded the first demonstration that it is possible to decode simultaneously the functional activity of multiple independent brain systems. Our comparison of univariate and multivariate decoding strategies, and our analysis of the influence of their decoding parameters, provides benchmarks and guidelines for future research on this topic.10aauditory processing10aElectrocorticography10amovement intention10arealtime decoding10asimultaneous decoding1 aGupta, Disha1 aHill, Jeremy, Jeremy1 aBrunner, Peter1 aGunduz, Aysegul1 aRitaccio, A L1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2508016103446nas a2200253 4500008004100000245011700041210006900158260001200227300001100239490000700250520261100257653003402868653002402902653003202926653002802958653002202986653003403008100002103042700001903063700002003082700002303102700001903125856004803144 2014 eng d00aSpatial and temporal relationships of electrocorticographic alpha and gamma activity during auditory processing.0 aSpatial and temporal relationships of electrocorticographic alph c08/2014 a188-950 v973 aNeuroimaging approaches have implicated multiple brain sites in musical perception, including the posterior part of the superior temporal gyrus and adjacent perisylvian areas. However, the detailed spatial and temporal relationship of neural signals that support auditory processing is largely unknown. In this study, we applied a novel inter-subject analysis approach to electrophysiological signals recorded from the surface of the brain (electrocorticography (ECoG)) in ten human subjects. This approach allowed us to reliably identify those ECoG features that were related to the processing of a complex auditory stimulus (i.e., continuous piece of music) and to investigate their spatial, temporal, and causal relationships. Our results identified stimulus-related modulations in the alpha (8-12 Hz) and high gamma (70-110 Hz) bands at neuroanatomical locations implicated in auditory processing. Specifically, we identified stimulus-related ECoG modulations in the alpha band in areas adjacent to primary auditory cortex, which are known to receive afferent auditory projections from the thalamus (80 of a total of 15,107 tested sites). In contrast, we identified stimulus-related ECoG modulations in the high gamma band not only in areas close to primary auditory cortex but also in other perisylvian areas known to be involved in higher-order auditory processing, and in superior premotor cortex (412/15,107 sites). Across all implicated areas, modulations in the high gamma band preceded those in the alpha band by 280 ms, and activity in the high gamma band causally predicted alpha activity, but not vice versa (Granger causality, p<1e(-8)). Additionally, detailed analyses using Granger causality identified causal relationships of high gamma activity between distinct locations in early auditory pathways within superior temporal gyrus (STG) and posterior STG, between posterior STG and inferior frontal cortex, and between STG and premotor cortex. Evidence suggests that these relationships reflect direct cortico-cortical connections rather than common driving input from subcortical structures such as the thalamus. In summary, our inter-subject analyses defined the spatial and temporal relationships between music-related brain activity in the alpha and high gamma bands. They provide experimental evidence supporting current theories about the putative mechanisms of alpha and gamma activity, i.e., reflections of thalamo-cortical interactions and local cortical neural activity, respectively, and the results are also in agreement with existing functional models of auditory processing.10aalpha and high gamma activity10aauditory processing10aelectrocorticography (ECoG)10afunctional connectivity10agranger causality10athalamo-cortical interactions1 aPotes, Cristhian1 aBrunner, Peter1 aGunduz, Aysegul1 aKnight, Robert, T.1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2476893301990nas a2200361 4500008004100000020002200041245002800063210002700091260003500118520105300153100002101206700001601227700001501243700002401258700001901282700001301301700001501314700001501329700001401344700001701358700001401375700001401389700001901403700001601422700001501438700002201453700002501475700001301500700001201513700002201525700001501547856006601562 2013 eng d a978-3-642-29745-800aBCI Software Platforms.0 aBCI Software Platforms bBiological and Medical Physics3 aIn this chapter, we provide an overview of publicly available software platforms for brain–computer interfaces. We have identified seven major BCI platforms and one platform specifically targeted towards feedback and stimulus presentation. We describe the intended target user group (which includes researchers, programmers, and end users), the most important features of each platform such as availability on different operating systems, licences, programming languages involved, supported devices, and so on. These seven platforms are: (1) BCI2000, (2) OpenViBE, (3) TOBI Common Implementation Platform (CIP), (4) BCILAB, (5) BCI++, (6) xBCI, and (7) BF++. The feedback framework is called Pyff. Our conclusion discusses possible synergies and future developments, such as combining different components of different platforms. With this overview, we hope to identify the strengths and weaknesses of each available platform, which should help anyone in the BCI research field in their decision which platform to use for their specific purposes.1 aBrunner, Clemens1 aAndreoni, G1 aBianchi, L1 aBlankertz, Benjamin1 aBreitwieser, C1 aKanoh, S1 aKothe, C A1 aLecuyer, A1 aMakeig, S1 aMellinger, J1 aPerego, P1 aRenard, Y1 aSchalk, Gerwin1 aSusila, I P1 aVenthur, B1 aMueller-Putz, G R1 aAllison, Brendan, Z.1 aDunne, S1 aLeeb, R1 aDel R. Millán, J1 aNijholt, A uhttp://link.springer.com/chapter/10.1007/978-3-642-29746-5_1600602nas a2200109 4500008004100000245012400041210006900165260001200234520010900246100001900355856011800374 2013 eng d00aBrain-Computer Interfaces Yesterday, Today, and Tomorrow: A Status Report of Bioengineering Research Partnership EB00850 aBrainComputer Interfaces Yesterday Today and Tomorrow A Status R c04/20133 aNational Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/brain-computer-interfaces-yesterday-today-and-tomorrow-status-report00527nas a2200109 4500008004100000245006000041210005900101260001200160520011900172100001900291856010700310 2013 eng d00aBrain-Computer Interfacing Using P300 Evoked Potentials0 aBrainComputer Interfacing Using P300 Evoked Potentials c04/20133 aGuest lecture in course Brain-Computer Interfaces, Electrical and Computer Engineering Department, NYU Poly1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/brain-computer-interfacing-using-p300-evoked-potentials-000539nas a2200109 4500008004100000245006000041210005900101260001200160520013300172100001900305856010500324 2013 eng d00aBrain-Computer Interfacing Using P300 Evoked Potentials0 aBrainComputer Interfacing Using P300 Evoked Potentials c04/20133 aGuest lecture in course Brain-Computer Interfaces, Departments of Neurosurgery/Bioengineering, University of Pennsylvania1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/brain-computer-interfacing-using-p300-evoked-potentials00395nas a2200109 4500008004100000245004200041210004200083260001200125520005100137100001900188856007800207 2013 eng d00aCommunicating Directly With the Brain0 aCommunicating Directly With the Brain c03/20133 aAnnual Gala of Fondazione Neurone, Rome, Italy1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/communicating-directly-brain00626nas a2200157 4500008004100000245011100041210006900152100001600221700001600237700002100253700002100274700001900295700001400314700001300328856012700341 2013 eng d00acortiQ – Clinical Software for Electrocorticographic Real-Time Functional Mapping of the Eloquent Cortex0 acortiQ Clinical Software for Electrocorticographic RealTime Func1 aPrueckl, R.1 aKapeller, C1 aPotes, Cristhian1 aKorostenskaja, M1 aSchalk, Gerwin1 aLee, K.H.1 aGuger, C uhttps://www.neurotechcenter.org/publications/cortiq-%E2%80%93-clinical-software-electrocorticographic-real-time-functional01883nas a2200217 4500008004100000022001400041245011000055210006900165260001200234300001100246490000900257520120500266100002001471700002401491700002101515700002601536700001901562700001501581700002101596856004801617 2013 eng d a1557-170X00aCortiQ - clinical software for electrocorticographic real-time functional mapping of the eloquent cortex.0 aCortiQ clinical software for electrocorticographic realtime func c07/2013 a6365-80 v20133 aPlanning for epilepsy surgery depends substantially on the localization of brain cortical areas responsible for sensory, motor, or cognitive functions, clinically also known as eloquent cortex. In this paper, we present the novel software package 'cortiQ' that allows clinicians to localize eloquent cortex, thus providing a safe margin for surgical resection with a low incidence of neurological deficits. This software can be easily used in addition to traditional mapping procedures such as the electrical cortical stimulation (ECS) mapping. The software analyses task-related changes in gamma activity recorded from implanted subdural electrocorticography electrodes using extensions to previously published methods. In this manuscript, we describe the system's architecture and workflow required to obtain a map of the eloquent cortex. We validate the system by comparing our mapping results with those acquired using ECS mapping in two subjects. Our results indicate that cortiQ reliably identifies eloquent cortex much faster (several minutes compared to an hour or more) than ECS mapping. Next-neighbour analyses show that there are no false positives and an average of 1.24% false negatives.1 aPrueckl, Robert1 aKapeller, Christoph1 aPotes, Cristhian1 aKorostenskaja, Milena1 aSchalk, Gerwin1 aLee, Ki, H1 aGuger, Christoph uhttp://www.ncbi.nlm.nih.gov/pubmed/2411119700474nas a2200109 4500008004100000245004900041210004800090260001200138520010100150100001900251856009400270 2013 eng d00aECoG-Based Neuroscience and Neuroengineering0 aECoGBased Neuroscience and Neuroengineering c05/20133 aCenter for Neuropharmacology and Neuroscience Seminar Series, Albany Medical College, Albany, NY1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/ecog-based-neuroscience-and-neuroengineering00417nas a2200109 4500008004100000245004800041210004800089260001200137520005000149100001900199856008900218 2013 eng d00aExciting Opportunities for Neuroengineering0 aExciting Opportunities for Neuroengineering c01/20133 aHershey Medical Center, Penn State University1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/exciting-opportunities-neuroengineering00455nas a2200109 4500008004100000245005200041210004700093260001200140520008400152100001900236856009000255 2013 eng d00aThe Exciting World of Brain-Computer Interfaces0 aExciting World of BrainComputer Interfaces c05/20133 aSociety of Physics Students, State University of New York at Albany, Albany, NY1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/exciting-world-brain-computer-interfaces00503nas a2200109 4500008004100000245006500041210006300106260001200169520008400181100001900265856010900284 2013 eng d00aLong-term Cortical Neuroprostheses: Prospects and Challenges0 aLongterm Cortical Neuroprostheses Prospects and Challenges c03/20133 a1st Bernstein Sparks Workshop on Cortical Neurointerfaces, Delmenhorst, Germany1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/long-term-cortical-neuroprostheses-prospects-and-challenges02782nas a2200193 4500008004100000022001400041245009600055210006900151260001200220300001400232490000700246520218000253100001702433700002502450700002402475700002202499700001902521856004802540 2013 eng d a1095-957200aA low-frequency oscillatory neural signal in humans encodes a developing decision variable.0 alowfrequency oscillatory neural signal in humans encodes a devel c12/2013 a795–8080 v833 aWe often make decisions based on sensory evidence that is accumulated over a period of time. How the evidence for such decisions is represented in the brain and how such a neural representation is used to guide a subsequent action are questions of considerable interest to decision sciences. The neural correlates of developing perceptual decisions have been thoroughly investigated in the oculomotor system of macaques who communicated their decisions using an eye movement. It has been found that the evidence informing a decision to make an eye movement is in part accumulated within the same oculomotor circuits that signal the upcoming eye movement. Recent evidence suggests that the somatomotor system may exhibit an analogous property for choices made using a hand movement. To investigate this possibility, we engaged humans in a decision task in which they integrated discrete quanta of sensory information over a period of time and signaled their decision using a hand movement or an eye movement. The discrete form of the sensory evidence allowed us to infer the decision variable on which subjects base their decision on each trial and to assess the neural processes related to each quantum of the incoming decision evidence. We found that a low-frequency electrophysiological signal recorded over centroparietal regions strongly encodes the decision variable inferred in this task, and that it does so specifically for hand movement choices. The signal ramps up with a rate that is proportional to the decision variable, remains graded by the decision variable throughout the delay period, reaches a common peak shortly before a hand movement, and falls off shortly after the hand movement. Furthermore, the signal encodes the polarity of each evidence quantum, with a short latency, and retains the response level over time. Thus, this neural signal shows properties of evidence accumulation. These findings suggest that the decision-related effects observed in the oculomotor system of the monkey during eye movement choices may share the same basic properties with the decision-related effects in the somatomotor system of humans during hand movement choices.1 aKubanek, Jan1 aSnyder, Lawrence, H.1 aBrunton, Bingni, W.1 aBrody, Carlos, D.1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2387249500460nas a2200109 4500008004100000245006200041210006200103260001200165520004800177100001900225856010600244 2013 eng d00aOpportunities in Computation Electrophysiological Imaging0 aOpportunities in Computation Electrophysiological Imaging c04/20133 aDepartment of Psychology, NYU, New York, NY1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/opportunities-computation-electrophysiological-imaging-000457nas a2200109 4500008004100000245006200041210006200103260001200165520004700177100001900224856010400243 2013 eng d00aOpportunities in Computation Electrophysiological Imaging0 aOpportunities in Computation Electrophysiological Imaging c02/20133 aMcGovern Institute for Brain Research, MIT1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/opportunities-computation-electrophysiological-imaging01886nas a2200397 4500008004100000245009000041210006900131260001200200300001300212490000700225520069800232653001800930653003100948653002500979653004301004653003201047653002101079653002201100653001801122100001801140700001901158700002201177700002001199700002501219700002101244700001601265700001901281700001901300700001901319700002001338700002401358700001801382700002101400700001901421856004801440 2013 eng d00aProceedings of the Fourth International Workshop on Advances in Electrocorticography.0 aProceedings of the Fourth International Workshop on Advances in  c11/2013 a259–680 v293 aThe Fourth International Workshop on Advances in Electrocorticography (ECoG) convened in New Orleans, LA, on October 11–12, 2012. The proceedings of the workshop serves as an accurate record of the most contemporary clinical and experimental work on brain surface recording and represents the insights of a unique multidisciplinary ensemble of expert clinicians and scientists. Presentations covered a broad range of topics, including innovations in passive functional mapping, increased understanding of pathologic high-frequency oscillations, evolving sensor technologies, a human trial of ECoG-driven brain–machine interface, as well as fresh insights into brain electrical stimulation.10aBrain Mapping10aBrain–computer interface10aElectrocorticography10aGamma-frequency electroencephalography10aHigh-frequency oscillations10aNeuroprosthetics10aSeizure detection10aSubdural grid1 aRitaccio, A L1 aBrunner, Peter1 aCrone, Nathan, E.1 aGunduz, Aysegul1 aHirsch, Lawrence, J.1 aKanwisher, Nancy1 aLitt, Brian1 aMiller, Kai, J1 aMorani, Daniel1 aParvizi, Josef1 aRamsey, Nick, F1 aRichner, Thomas, J.1 aTandon, Niton1 aWilliams, Justin1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2403489900398nas a2200133 4500008004100000245005500041210005200096260001200148300001000160490000800170100001900178700001900197856004800216 2013 eng d00aToward Gaze-Independent Brain-Computer Interfaces.0 aToward GazeIndependent BrainComputer Interfaces c05/2013 a831-30 v1251 aBrunner, Peter1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2346543102234nas a2200181 4500008004100000245005700041210005200098260001200150300001400162490000600176520172300182100001701905700001901922700002001941700001901961700001901980856005301999 2013 eng d00aThe Tracking of Speech Envelope in the Human Cortex.0 aTracking of Speech Envelope in the Human Cortex c01/2013 ae53398 - 0 v83 aHumans are highly adept at processing speech. Recently, it has been shown that slow temporal information in speech (i.e., the envelope of speech) is critical for speech comprehension. Furthermore, it has been found that evoked electric potentials in human cortex are correlated with the speech envelope. However, it has been unclear whether this essential linguistic feature is encoded differentially in specific regions, or whether it is represented throughout the auditory system. To answer this question, we recorded neural data with high temporal resolution directly from the cortex while human subjects listened to a spoken story. We found that the gamma activity in human auditory cortex robustly tracks the speech envelope. The effect is so marked that it is observed during a single presentation of the spoken story to each subject. The effect is stronger in regions situated relatively early in the auditory pathway (belt areas) compared to other regions involved in speech processing, including the superior temporal gyrus (STG) and the posterior inferior frontal gyrus (Broca's region). To further distinguish whether speech envelope is encoded in the auditory system as a phonological (speech-related), or instead as a more general acoustic feature, we also probed the auditory system with a melodic stimulus. We found that belt areas track melody envelope weakly, and as the only region considered. Together, our data provide the first direct electrophysiological evidence that the envelope of speech is robustly tracked in non-primary auditory cortex (belt areas in particular), and suggest that the considered higher-order regions (STG and Broca's region) partake in a more abstract linguistic analysis.1 aKubanek, Jan1 aBrunner, Peter1 aGunduz, Aysegul1 aPoeppel, David1 aSchalk, Gerwin uhttp://dx.doi.org/10.1371%2Fjournal.pone.005339800515nas a2200109 4500008004100000245007800041210006900119260001500188520006100203100001900264856012200283 2012 eng d00aBCI2000: A General-Purpose BCI System and Its Application to ECoG Signals0 aBCI2000 A GeneralPurpose BCI System and Its Application to ECoG  c10/13/20123 ag.tec Brain-Computer Interface Workshop, New Orleans, LA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/bci2000-general-purpose-bci-system-and-its-application-ecog-signals00514nas a2200109 4500008004100000245007800041210006900119260001200188520006100200100001900261856012400280 2012 eng d00aBCI2000: A General-Purpose BCI System and its Application to ECoG Signals0 aBCI2000 A GeneralPurpose BCI System and its Application to ECoG  c10/20123 ag.tec Brain-Computer Interface Workshop, New Orleans, LA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/bci2000-general-purpose-bci-system-and-its-application-ecog-signals-001311nas a2200193 4500008004100000022002500041245005000066210004900116260002800165520065200193653001800845653003000863653000900893653003700902100002100939700002100960700001900981856011701000 2012 eng d aISBN: 978-019538885500aBCIs That Use Electrocorticographic Activity.0 aBCIs That Use Electrocorticographic Activity bOxford University Press3 aThis chapter discusses the potential of electrocorticography (ECoG) as a clinically useful brain-computer interface signal modality. ECoG has greater amplitude, higher topographical resolution, and a much broader frequency range than scalp-recorded electroencephalography and is less susceptible to artifacts. With current and foreseeable recording methodologies, ECoG is likely to have greater long-term stability than intracortically recorded signals. Furthermore, it can more readily be recorded from larger cortical areas, and it requires much lower digitization rates, thus greatly reducing the power requirements of wholly implanted systems.10abrain signals10abrain-computer interfaces10aECoG10aintracortically recorded signals1 aWolpaw, Jonathan1 aWinter-Wolpaw, E1 aSchalk, Gerwin uhttp://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780195388855.001.0001/acprof-9780195388855-chapter-01500575nas a2200109 4500008004100000245006000041210005900101260001500160520016100175100001900336856011000355 2012 eng d00aBrain-Computer Interfacing Using P300 Evoked Potentials0 aBrainComputer Interfacing Using P300 Evoked Potentials c03/20/20123 aGuest lecture in course Brain-Computer Interfaces, Departments of Neurosurgery/Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/brain-computer-interfacing-using-p300-evoked-potentials00549nas a2200109 4500008004100000245006000041210005900101260001500160520013300175100001900308856011200327 2012 eng d00aBrain-Computer Interfacing Using P300 Evoked Potentials0 aBrainComputer Interfacing Using P300 Evoked Potentials c03/21/20123 aGuest lecture in course Brain-Computer Interfaces, Electrical and Computer Engineering Department, NYU poly, New York, NY1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/brain-computer-interfacing-using-p300-evoked-potentials-000495nas a2200109 4500008004100000245004200041210004200083260001500125520014300140100001900283856008300302 2012 eng d00aCommunicating Directly With the Brain0 aCommunicating Directly With the Brain c06/15/20123 aIntroductory lecture at the initial public presentation of the €20m Italian project "cyber brain." Chamber of Commerce, Avellino, Italy.1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/communicating-directly-brain02832nas a2200253 4500008004100000022001400041245012700055210006900182260001200251490000600263520200400269653002302273653003802296653002902334653002302363653001702386653001702403100002502420700002102445700002402466700002102490700001902511856004802530 2012 eng d a1662-453X00aCommunication and control by listening: towards optimal design of a two-class auditory streaming brain-computer interface.0 aCommunication and control by listening towards optimal design of c12/20120 v63 aMost brain-computer interface (BCI) systems require users to modulate brain signals in response to visual stimuli. Thus, they may not be useful to people with limited vision, such as those with severe paralysis. One important approach for overcoming this issue is auditory streaming, an approach whereby a BCI system is driven by shifts of attention between two simultaneously presented auditory stimulus streams. Motivated by the long-term goal of translating such a system into a reliable, simple yes-no interface for clinical usage, we aim to answer two main questions. First, we asked which of two previously published variants provides superior performance: a fixed-phase (FP) design in which the streams have equal period and opposite phase, or a drifting-phase (DP) design where the periods are unequal. We found FP to be superior to DP (p = 0.002): average performance levels were 80 and 72% correct, respectively. We were also able to show, in a pilot with one subject, that auditory streaming can support continuous control and neurofeedback applications: by shifting attention between ongoing left and right auditory streams, the subject was able to control the position of a paddle in a computer game. Second, we examined whether the system is dependent on eye movements, since it is known that eye movements and auditory attention may influence each other, and any dependence on the ability to move one’s eyes would be a barrier to translation to paralyzed users. We discovered that, despite instructions, some subjects did make eye movements that were indicative of the direction of attention. However, there was no correlation, across subjects, between the reliability of the eye movement signal and the reliability of the BCI system, indicating that our system was configured to work independently of eye movement. Together, these findings are an encouraging step forward toward BCIs that provide practical communication and control options for the most severely paralyzed users. 10aauditory attention10aauditory event-related potentials10abrain-computer interface10adichotic listening10aN1 potential10aP3 potential1 aHill, Jeremy, Jeremy1 aMoinuddin, Aisha1 aHäuser, Ann-Katrin1 aKienzle, Stephan1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2326731201823nas a2200253 4500008004100000022001400041245009200055210006900147260001200216300001200228490000700240520106200247653002101309653003201330653002901362100002001391700001901411700001601430700001901446700001801465700001901483700001901502856004801521 2012 eng d a1095-957200aDecoding covert spatial attention using electrocorticographic (ECoG) signals in humans.0 aDecoding covert spatial attention using electrocorticographic EC c05/2012 a2285-930 v603 a

This study shows that electrocorticographic (ECoG) signals recorded from the surface of the brain provide detailed information about shifting of visual attention and its directional orientation in humans. ECoG allows for the identification of the cortical areas and time periods that hold the most information about covert attentional shifts. Our results suggest a transient distributed fronto-parietal mechanism for orienting of attention that is represented by different physiological processes. This neural mechanism encodes not only whether or not a subject shifts their attention to a location, but also the locus of attention. This work contributes to our understanding of the electrophysiological representation of attention in humans. It may also eventually lead to brain-computer interfaces (BCIs) that optimize user interaction with their surroundings or that allow people to communicate choices simply by shifting attention to them.

10acovert attention10aelectrocorticography (ECoG)10avisual spatial attention1 aGunduz, Aysegul1 aBrunner, Peter1 aDaitch, Amy1 aLeuthardt, E C1 aRitaccio, A L1 aPesaran, Bijan1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2236633301958nas a2200253 4500008004100000245010000041210006900141260001200210490000600222520117500228653002901403653000901432653003401441653003001475653002401505653002901529100001201558700002001570700001901590700001801609700001001627700001901637856004801656 2012 eng d00aDecoding Onset and Direction of Movements using Electrocorticographic (ECoG) Signals in Humans.0 aDecoding Onset and Direction of Movements using Electrocorticogr c08/20120 v53 aCommunication of intent usually requires motor function. This requirement can be limiting when a person is engaged in a task, or prohibitive for some people suffering from neuromuscular disorders. Determining a person's intent, e.g., where and when to move, from brain signals rather than from muscles would have important applications in clinical or other domains. For example, detection of the onset and direction of intended movements may provide the basis for restoration of simple grasping function in people with chronic stroke, or could be used to optimize a user's interaction with the surrounding environment. Detecting the onset and direction of actual movements are a first step in this direction. In this study, we demonstrate that we can detect the onset of intended movements and their direction using electrocorticographic (ECoG) signals recorded from the surface of the cortex in humans. We also demonstrate in a simulation that the information encoded in ECoG about these movements may improve performance in a targeting task. In summary, the results in this paper suggest that detection of intended movement is possible, and may serve useful functions.10abrain computer interface10aECoG10amovement direction prediction10amovement onset prediction10aneurorehabilitation10aperformance augmentation1 aWang, Z1 aGunduz, Aysegul1 aBrunner, Peter1 aRitaccio, A L1 aJi, Q1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2289105803280nas a2200229 4500008004100000022001400041245012300055210006900178260001200247300001000259490000700269520254700276653002402823653003202847653002402879653002002903100002102923700002002944700001902964700001902983856004803002 2012 eng d a1095-957200aDynamics of electrocorticographic (ECoG) activity in human temporal and frontal cortical areas during music listening.0 aDynamics of electrocorticographic ECoG activity in human tempora c07/2012 a841-80 v613 a

Previous studies demonstrated that brain signals encode information about specific features of simple auditory stimuli or of general aspects of natural auditory stimuli. How brain signals represent the time course of specific features in natural auditory stimuli is not well understood. In this study, we show in eight human subjects that signals recorded from the surface of the brain (electrocorticography (ECoG)) encode information about the sound intensity of music. ECoG activity in the high gamma band recorded from the posterior part of the superior temporal gyrus as well as from an isolated area in the precentral gyrus was observed to be highly correlated with the sound intensity of music. These results not only confirm the role of auditory cortices in auditory processing but also point to an important role of premotor and motor cortices. They also encourage the use of ECoG activity to study more complex acoustic features of simple or natural auditory stimuli.

10aauditory processing10aelectrocorticography (ECoG)10ahigh gamma activity10asound intensity1 aPotes, Cristhian1 aGunduz, Aysegul1 aBrunner, Peter1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2253760000450nas a2200109 4500008004100000245004900041210004800090260001500138520006900153100001900222856009900241 2012 eng d00aECoG-Based Neuroscience and Neuroengineering0 aECoGBased Neuroscience and Neuroengineering c09/19/20123 aBBCI Workshop 2012, Advances in Neurotechnology, Berlin, Germany1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/ecog-based-neuroscience-and-neuroengineering02208nas a2200265 4500008004100000022001400041245006800055210006500123260001200188300000900200490000800209520143000217653001501647653003001662653001801692653002501710653001701735653003601752100002601788700002101814700001901835700001901854700002101873856004801894 2012 eng d a1432-110600aElectrocorticographic (ECoG) Correlates of Human Arm Movements.0 aElectrocorticographic ECoG Correlates of Human Arm Movements c11/2012 a1-100 v2233 aInvasive and non-invasive brain-computer interface (BCI) studies have long focused on the motor cortex for kinematic control of artificial devices. Most of these studies have used single-neuron recordings or electroencephalography (EEG). Electrocorticography (ECoG) is a relatively new recording modality in BCI research that has primarily been built on successes in EEG recordings. We built on prior experiments related to single-neuron recording and quantitatively compare the extent to which different brain regions reflect kinematic tuning parameters of hand speed, direction, and velocity in both a reaching and tracing task in humans. Hand and arm movement experiments using ECoG have shown positive results before, but the tasks were not designed to tease out which kinematics are encoded. In non-human primates, the relationships among these kinematics have been more carefully documented, and we sought to begin elucidating that relationship in humans using ECoG. The largest modulation in ECoG activity for direction, speed, and velocity representation was found in the primary motor cortex. We also found consistent cosine tuning across both tasks, to hand direction and velocity in the high gamma band (70-160 Hz). Thus, the results of this study clarify the neural substrates involved in encoding aspects of motor preparation and execution and confirm the important role of the motor cortex in BCI applications.10aarm tuning10abrain-computer interfaces10acosine tuning10aElectrocorticography10aMotor Cortex10asubdural electroencephalography1 aAnderson, Nicholas, R1 aBlakely, Timothy1 aSchalk, Gerwin1 aLeuthardt, E C1 aMoran, Daniel, W uhttp://www.ncbi.nlm.nih.gov/pubmed/2300136900535nas a2200109 4500008004100000245006100041210006100102260001500163520012000178100001900298856010800317 2012 eng d00aExciting Adventures in Neuroscience and Neuroengineering0 aExciting Adventures in Neuroscience and Neuroengineering c04/19/20123 aColloquium at Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland OH1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/exciting-adventures-neuroscience-and-neuroengineering00451nas a2200109 4500008004100000245006100041210006100102260001500163520003600178100001900214856010800233 2012 eng d00aExciting Directions in Neuroscience and Neuroengineering0 aExciting Directions in Neuroscience and Neuroengineering c04/18/20123 aKent State University, Kent, OH1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/exciting-directions-neuroscience-and-neuroengineering00466nas a2200109 4500008004100000245006400041210006400105260001500169520004200184100001900226856011100245 2012 eng d00aExciting Opportunities in Neuroscience and Neuroengineering0 aExciting Opportunities in Neuroscience and Neuroengineering c01/27/20123 aUniversity of Washington, Seattle, WA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/exciting-opportunities-neuroscience-and-neuroengineering00453nas a2200109 4500008004100000245005200041210004700093260001500140520007400155100001900229856009500248 2012 eng d00aThe Exciting World of Brain-Computer Interfaces0 aExciting World of BrainComputer Interfaces c08/01/20123 aWadsworth Center Research Experience for Undergraduates (REU) program1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/exciting-world-brain-computer-interfaces00449nas a2200109 4500008004100000245005200041210004700093260001500140520006800155100001900223856009700242 2012 eng d00aThe Exciting World of Brain-Computer Interfaces0 aExciting World of BrainComputer Interfaces c10/19/20123 aLecture in course Science in the News, Sage College, Albany, NY1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/exciting-world-brain-computer-interfaces-000517nas a2200109 4500008004100000245005300041210004800094260001500142520013500157100001900292856009600311 2012 eng d00aThe Exciting World of Brain-Computer Interfacing0 aExciting World of BrainComputer Interfacing c05/10/20123 aKeynote Address, Workshop in "Solving the Mystery of how the Brian Works." Walt Disney Pavilion, Florida Hospital for Children, FL1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/exciting-world-brain-computer-interfacing00445nas a2200109 4500008004100000245004100041210004100082260001200123520009100135100001900226856009000245 2012 eng d00aFuture Aspects of Functional Mapping0 aFuture Aspects of Functional Mapping c10/20123 a1st International Workshop on Functional Mapping with ECoG, New Orleans, LA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/future-aspects-functional-mapping-000343nas a2200097 4500008004100000245004100041210004100082260001500123100001900138856008800157 2012 eng d00aFuture Aspects of Functional Mapping0 aFuture Aspects of Functional Mapping c10/15/20121 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/future-aspects-functional-mapping00508nas a2200157 4500008004100000022001900041245004000060210003900100260002800139100001900167700001300186700002000199700002100219700002100240856008900261 2012 eng d a978-019538885500aHardware and Software Technologies.0 aHardware and Software Technologies bOxford University Press1 aSchalk, Gerwin1 aGuger, C1 aWilson, Adam, J1 aWolpaw, Jonathan1 aWinter-Wolpaw, E uhttps://www.neurotechcenter.org/publications/2012/hardware-and-software-technologies00377nas a2200133 4500008004100000245003100041210003100072260001200103100001200115700001100127700001900138700001000157856007600167 2012 eng d00aLearning with Target Prior0 aLearning with Target Prior c11/20121 aWang, Z1 aLyu, S1 aSchalk, Gerwin1 aJi, Q uhttps://www.neurotechcenter.org/publications/2012/learning-target-prior00465nas a2200109 4500008004100000245005100041210005100092260001500143520008000158100001900238856009800257 2012 eng d00aPast and Present Aspects of Functional Mapping0 aPast and Present Aspects of Functional Mapping c10/15/20123 a1st International Workshop on Functional Mapping with ECoG, New Orleans, LA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/past-and-present-aspects-functional-mapping00466nas a2200109 4500008004100000245005000041210005000091260001500141520008400156100001900240856009700259 2012 eng d00aPerspectives on ECoG Research and Application0 aPerspectives on ECoG Research and Application c10/12/20123 a4th International Workshop on Advances in Electrocorticography, New Orleans, LA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/perspectives-ecog-research-and-application00456nas a2200109 4500008004100000245005500041210005400096260001500150520006000165100001900225856010200244 2012 eng d00aPrinciples of Real-Time Passive Functional Mapping0 aPrinciples of RealTime Passive Functional Mapping c11/27/20123 aDepartment of Neurology, Yale University, New Haven, CT1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/principles-real-time-passive-functional-mapping01889nas a2200469 4500008004100000022001400041245008900055210006900144260001200213300001100225490000700236520052900243653001800772653002900790653002500819653004300844653003100887653002100918653002200939653001800961100001800979700002300997700002001020700001901040700001801059700002201077700002001099700001701119700002301136700002001159700001601179700001801195700002101213700001901234700001701253700001901270700002201289700002001311700002101331700001901352856004801371 2012 eng d a1525-506900aProceedings of the Third International Workshop on Advances in Electrocorticography.0 aProceedings of the Third International Workshop on Advances in E c12/2012 a605-130 v253 aThe Third International Workshop on Advances in Electrocorticography (ECoG) was convened in Washington, DC, on November 10-11, 2011. As in prior meetings, a true multidisciplinary fusion of clinicians, scientists, and engineers from many disciplines gathered to summarize contemporary experiences in brain surface recordings. The proceedings of this meeting serve as evidence of a very robust and transformative field but will yet again require revision to incorporate the advances that the following year will surely bring.10aBrain Mapping10abrain-computer interface10aElectrocorticography10aGamma-frequency electroencephalography10ahigh-frequency oscillation10aNeuroprosthetics10aSeizure detection10aSubdural grid1 aRitaccio, A L1 aBeauchamp, Michael1 aBosman, Conrado1 aBrunner, Peter1 aChang, Edward1 aCrone, Nathan, E.1 aGunduz, Aysegul1 aGupta, Disha1 aKnight, Robert, T.1 aLeuthardt, Eric1 aLitt, Brian1 aMoran, Daniel1 aOjemann, Jeffrey1 aParvizi, Josef1 aRamsey, Nick1 aRieger, Jochem1 aViventi, Jonathan1 aVoytek, Bradley1 aWilliams, Justin1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2316009600413nas a2200109 4500008004100000245004400041210004300085260001500128520004700143100001900190856009400209 2012 eng d00aReal-Time Functional Mapping Using ECoG0 aRealTime Functional Mapping Using ECoG c10/15/20123 ag.tec ECoG/Spike Workshop, New Orleans, LA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/real-time-functional-mapping-using-ecog10628nas a2200313 4500008004100000022001400041245012900055210006900184260001200253520965800265653001209923653003109935653002509966653002409991653002910015653001310044653003110057653000810088653001710096653001310113100002510126700001710151700001910168700002010187700002210207700001810229700001910247856004810266 2012 eng d a1940-087X00aRecording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping.0 aRecording Human Electrocorticographic ECoG Signals for Neuroscie c05/20123 a

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), magnetoencephalography or functional magnetic-resonance imaging. These techniques have either inherently low temporal or low spatial resolution, and suffer from low signal-to-noise ratio and/or poor high-frequency sensitivity. Thus, they are suboptimal for exploring the short-lived spatio-temporal dynamics of many of the underlying brain processes. In contrast, the invasive technique of electrocorticography (ECoG) provides brain signals that have an exceptionally high signal-to-noise ratio, less susceptibility to artifacts than EEG, and a high spatial and temporal resolution (i.e., <1 cm/<1 millisecond, respectively). ECoG involves measurement of electrical brain signals using electrodes that are implanted subdurally on the surface of the brain. Recent studies have shown that ECoG amplitudes in certain frequency bands carry substantial information about task-related activity, such as motor execution and planning, auditory processing and visual-spatial attention. Most of this information is captured in the high gamma range (around 70-110 Hz). Thus, gamma activity has been proposed as a robust and general indicator of local cortical function. ECoG can also reveal functional connectivity and resolve finer task-related spatial-temporal dynamics, thereby advancing our understanding of large-scale cortical processes. It has especially proven useful for advancing brain-computer interfacing (BCI) technology for decoding a user's intentions to enhance or improve communication and control. Nevertheless, human ECoG data are often hard to obtain because of the risks and limitations of the invasive procedures involved, and the need to record within the constraints of clinical settings. Still, clinical monitoring to localize epileptic foci offers a unique and valuable opportunity to collect human ECoG data. We describe our methods for collecting recording ECoG, and demonstrate how to use these signals for important real-time applications such as clinical mapping and brain-computer interfacing. Our example uses the BCI2000 software platform and the SIGFRIED method, an application for real-time mapping of brain functions. This procedure yields information that clinicians can subsequently use to guide the complex and laborious process of functional mapping by electrical stimulation. PREREQUISITES AND PLANNING: Patients with drug-resistant partial epilepsy may be candidates for resective surgery of an epileptic focus to minimize the frequency of seizures. Prior to resection, the patients undergo monitoring using subdural electrodes for two purposes: first, to localize the epileptic focus, and second, to identify nearby critical brain areas (i.e., eloquent cortex) where resection could result in long-term functional deficits. To implant electrodes, a craniotomy is performed to open the skull. Then, electrode grids and/or strips are placed on the cortex, usually beneath the dura. A typical grid has a set of 8 x 8 platinum-iridium electrodes of 4 mm diameter (2.3 mm exposed surface) embedded in silicon with an inter-electrode distance of 1cm. A strip typically contains 4 or 6 such electrodes in a single line. The locations for these grids/strips are planned by a team of neurologists and neurosurgeons, and are based on previous EEG monitoring, on a structural MRI of the patient's brain, and on relevant factors of the patient's history. Continuous recording over a period of 5-12 days serves to localize epileptic foci, and electrical stimulation via the implanted electrodes allows clinicians to map eloquent cortex. At the end of the monitoring period, explantation of the electrodes and therapeutic resection are performed together in one procedure. In addition to its primary clinical purpose, invasive monitoring also provides a unique opportunity to acquire human ECoG data for neuroscientific research. The decision to include a prospective patient in the research is based on the planned location of their electrodes, on the patient's performance scores on neuropsychological assessments, and on their informed consent, which is predicated on their understanding that participation in research is optional and is not related to their treatment. As with all research involving human subjects, the research protocol must be approved by the hospital's institutional review board. The decision to perform individual experimental tasks is made day-by-day, and is contingent on the patient's endurance and willingness to participate. Some or all of the experiments may be prevented by problems with the clinical state of the patient, such as post-operative facial swelling, temporary aphasia, frequent seizures, post-ictal fatigue and confusion, and more general pain or discomfort. At the Epilepsy Monitoring Unit at Albany Medical Center in Albany, New York, clinical monitoring is implemented around the clock using a 192-channel Nihon-Kohden Neurofax monitoring system. Research recordings are made in collaboration with the Wadsworth Center of the New York State Department of Health in Albany. Signals from the ECoG electrodes are fed simultaneously to the research and the clinical systems via splitter connectors. To ensure that the clinical and research systems do not interfere with each other, the two systems typically use separate grounds. In fact, an epidural strip of electrodes is sometimes implanted to provide a ground for the clinical system. Whether research or clinical recording system, the grounding electrode is chosen to be distant from the predicted epileptic focus and from cortical areas of interest for the research. Our research system consists of eight synchronized 16-channel g.USBamp amplifier/digitizer units (g.tec, Graz, Austria). These were chosen because they are safety-rated and FDA-approved for invasive recordings, they have a very low noise-floor in the high-frequency range in which the signals of interest are found, and they come with an SDK that allows them to be integrated with custom-written research software. In order to capture the high-gamma signal accurately, we acquire signals at 1200Hz sampling rate-considerably higher than that of the typical EEG experiment or that of many clinical monitoring systems. A built-in low-pass filter automatically prevents aliasing of signals higher than the digitizer can capture. The patient's eye gaze is tracked using a monitor with a built-in Tobii T-60 eye-tracking system (Tobii Tech., Stockholm, Sweden). Additional accessories such as joystick, bluetooth Wiimote (Nintendo Co.), data-glove (5(th) Dimension Technologies), keyboard, microphone, headphones, or video camera are connected depending on the requirements of the particular experiment. Data collection, stimulus presentation, synchronization with the different input/output accessories, and real-time analysis and visualization are accomplished using our BCI2000 software. BCI2000 is a freely available general-purpose software system for real-time biosignal data acquisition, processing and feedback. It includes an array of pre-built modules that can be flexibly configured for many different purposes, and that can be extended by researchers' own code in C++, MATLAB or Python. BCI2000 consists of four modules that communicate with each other via a network-capable protocol: a Source module that handles the acquisition of brain signals from one of 19 different hardware systems from different manufacturers; a Signal Processing module that extracts relevant ECoG features and translates them into output signals; an Application module that delivers stimuli and feedback to the subject; and the Operator module that provides a graphical interface to the investigator. A number of different experiments may be conducted with any given patient. The priority of experiments will be determined by the location of the particular patient's electrodes. However, we usually begin our experimentation using the SIGFRIED (SIGnal modeling For Realtime Identification and Event Detection) mapping method, which detects and displays significant task-related activity in real time. The resulting functional map allows us to further tailor subsequent experimental protocols and may also prove as a useful starting point for traditional mapping by electrocortical stimulation (ECS). Although ECS mapping remains the gold standard for predicting the clinical outcome of resection, the process of ECS mapping is time consuming and also has other problems, such as after-discharges or seizures. Thus, a passive functional mapping technique may prove valuable in providing an initial estimate of the locus of eloquent cortex, which may then be confirmed and refined by ECS. The results from our passive SIGFRIED mapping technique have been shown to exhibit substantial concurrence with the results derived using ECS mapping. The protocol described in this paper establishes a general methodology for gathering human ECoG data, before proceeding to illustrate how experiments can be initiated using the BCI2000 software platform. Finally, as a specific example, we describe how to perform passive functional mapping using the BCI2000-based SIGFRIED system.

10aBCI200010abrain-computer interfacing10aElectrocorticography10aepilepsy monitoring10afunctional brain mapping10aissue 6410aMagnetic Resonance Imaging10aMRI10aneuroscience10aSIGFRIED1 aHill, Jeremy, Jeremy1 aGupta, Disha1 aBrunner, Peter1 aGunduz, Aysegul1 aAdamo, Matthew, A1 aRitaccio, A L1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2278213101769nas a2200373 4500008004100000245003800041210003700079260001200116300000900128490000600137520083700143653000800980653002900988653001601017100001801033700001601051700001501067700002101082700002101103700002101124700001201145700001501157700001601172700002001188700001301208700002101221700001501242700001901257700001601276700001601292700001501308700002401323856004801347 2012 eng d00aReview of the BCI Competition IV.0 aReview of the BCI Competition IV c07/2012 a1-310 v63 aThe BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.10aBCI10abrain-computer interface10acompetition1 aTangermann, M1 aMuller, K R1 aAertsen, A1 aBirbaumer, Niels1 aBraun, Christoph1 aBrunner, Clemens1 aLeeb, R1 aMehring, C1 aMiller, K J1 aMueller-Putz, G1 aNolte, G1 aPfurtscheller, G1 aPreissl, H1 aSchalk, Gerwin1 aSchlögl, A1 aVidaurre, C1 aWaldert, S1 aBlankertz, Benjamin uhttp://www.ncbi.nlm.nih.gov/pubmed/2281165701835nas a2200241 4500008004100000022001400041245005400055210005200109260001200161300000900173490000600182520120500188653001201393653001001405653001601415653001101431653001301442653002801455100001801483700002501501700001901526856004801545 2012 eng d a2154-228700aSilent Communication: toward using brain signals.0 aSilent Communication toward using brain signals c01/2012 a43-60 v33 a

From the 1980s movie Firefox to the more recent Avatar, popular science fiction has speculated about the possibility of a persons thoughts being read directly from his or her brain. Such braincomputer interfaces (BCIs) might allow people who are paralyzed to communicate with and control their environment, and there might also be applications in military situations wherever silent user-to-user communication is desirable. Previous studies have shown that BCI systems can use brain signals related to movements and movement imagery or attention-based character selection. Although these systems have successfully demonstrated the possibility to control devices using brain function, directly inferring which word a person intends to communicate has been elusive. A BCI using imagined speech might provide such a practical, intuitive device. Toward this goal, our studies to date addressed two scientific questions: (1) Can brain signals accurately characterize different aspects of speech? (2) Is it possible to predict spoken or imagined words or their components using brain signals?

10aAnimals10aBrain10aBrain Waves10aHumans10aMovement10aUser-Computer Interface1 aPei, Xiao-Mei1 aHill, Jeremy, Jeremy1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2234495102945nas a2200289 4500008004100000022001400041245010600055210006900161260001200230300000700242490000600249520210500255653001102360653002502371653001802396653001002414653001102424100001902435700001802454700002402472700002202496700001802518700002902536700002302565700001902588856004802607 2012 eng d a1662-516100aTemporal evolution of gamma activity in human cortex during an overt and covert word repetition task.0 aTemporal evolution of gamma activity in human cortex during an o c05/2012 a990 v63 a

Several scientists have proposed different models for cortical processing of speech. Classically, the regions participating in language were thought to be modular with a linear sequence of activations. More recently, modern theoretical models have posited a more hierarchical and distributed interaction of anatomic areas for the various stages of speech processing. Traditional imaging techniques can only define the location or time of cortical activation, which impedes the further evaluation and refinement of these models. In this study, we take advantage of recordings from the surface of the brain [electrocorticography (ECoG)], which can accurately detect the location and timing of cortical activations, to study the time course of ECoG high gamma (HG) modulations during an overt and covert word repetition task for different cortical areas. For overt word production, our results show substantial perisylvian cortical activations early in the perceptual phase of the task that were maintained through word articulation. However, this broad activation is attenuated during the expressive phase of covert word repetition. Across the different repetition tasks, the utilization of the different cortical sites within the perisylvian region varied in the degree of activation dependent on which stimulus was provided (auditoryor visual cue) and whether the word was to be spoken or imagined. Taken together, the data support current models of speech that have been previously described with functional imaging. Moreover, this study demonstrates that the broad perisylvian speech network activates early and maintains suprathreshold activation throughout the word repetition task that appears to be modulated by the demands of different conditions.

10acortex10aElectrocorticography10agamma rhythms10ahuman10aSpeech1 aLeuthardt, E C1 aPei, Xiao-Mei1 aBreshears, Jonathan1 aGaona, Charles, M1 aSharma, Mohit1 aFreudenberg, Zachary, V.1 aBarbour, Dennis, L1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2256331100445nas a2200109 4500008004100000245003600041210003600077260001500113520010900128100001900237856007900256 2012 eng d00aUsing Machines to Read the Mind0 aUsing Machines to Read the Mind c02/09/20123 aDepartment of Neurology and Neurological Services, Stanford University School of Medicine, Palo Alto, CA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2012/using-machines-read-mind00414nas a2200109 4500008004100000245003000041210003000071260001500101520008900116100001900205856008000224 2011 eng d00aAdvanced BCI2000 Concepts0 aAdvanced BCI2000 Concepts c05/18/20113 a8th BCI2000 Workshop, University Medical Center, Utrecht, The Netherlands1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/advanced-bci2000-concepts00483nas a2200109 4500008004100000245009100041210006900132100001800201700001900219700001000238856012500248 2011 eng d00aAnatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals0 aAnatomically Constrained Decoding of Finger Flexion from Electro1 aWang, Zuoguan1 aSchalk, Gerwin1 aJi, Q uhttps://www.neurotechcenter.org/publications/2011/anatomically-constrained-decoding-finger-flexion-electrocorticographic00353nas a2200109 4500008004100000245001200041210001200053260001500065520008200080100001900162856006200181 2011 eng d00aBCI20000 aBCI2000 c09/22/20113 aFBNCI Cluster Workshop for Roadmap Development, Graz University of Technology1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/bci200000516nas a2200109 4500008004100000245007800041210006900119260001500188520006000203100001900263856012400282 2011 eng d00aBCI2000: A General-Purpose BCI System and its Application to ECoG Signals0 aBCI2000 A GeneralPurpose BCI System and its Application to ECoG  c11/12/20113 ag.tec Brain-Computer Interface Workshop, Washington, DC1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/bci2000-general-purpose-bci-system-and-its-application-ecog-signals-000532nas a2200109 4500008004100000245007800041210006900119260001500188520007800203100001900281856012200300 2011 eng d00aBCI2000: A General-Purpose BCI System and its Application to ECoG Signals0 aBCI2000 A GeneralPurpose BCI System and its Application to ECoG  c08/30/20113 ag.tec Brain-Computer Interface Workshop, IEEE EMBC Conference, Boston, MA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/bci2000-general-purpose-bci-system-and-its-application-ecog-signals00584nas a2200109 4500008004100000245008400041210006900125260001500194520012200209100001900331856012400350 2011 eng d00aBrain-Computer Interfaces: Integrating Bioengineering and Neuroscience Research0 aBrainComputer Interfaces Integrating Bioengineering and Neurosci c04/03/20113 aKeynote, 37th Annual Northeast Bioengineering Conference, Rensselaer Polytechnic Institute, Troy, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/brain-computer-interfaces-integrating-bioengineering-and-neuroscience00541nas a2200109 4500008004100000245007500041210006900116260001500185520010700200100001900307856010500326 2011 eng d00aBrain-Computer Interfaces: The Hope, The Hype, The Power, and The Pain0 aBrainComputer Interfaces The Hope The Hype The Power and The Pai c05/21/20113 aBrain-Computer Interfacing in 2011, Rudolf Magnus Institute for Neuroscience, Utrecht, The Netherlands1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/brain-computer-interfaces-hope-hype-power-and-pain02258nas a2200193 4500008004100000022001400041245006700055210006500122260001200187300001100199490000600210520166100216653003501877653003401912653003201946100001901978700001901997856004802016 2011 eng d a1941-118900aBrain-computer interfaces using electrocorticographic signals.0 aBraincomputer interfaces using electrocorticographic signals c10/2011 a140-540 v43 a

Many studies over the past two decades have shown that people and animals can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems measure specific features of brain activity and translate them into control signals that drive an output. The sensor modalities that have most commonly been used in BCI studies have been electroencephalographic (EEG) recordings from the scalp and single-neuron recordings from within the cortex. Over the past decade, an increasing number of studies has explored the use of electrocorticographic (ECoG) activity recorded directly from the surface of the brain. ECoG has attracted substantial and increasing interest, because it has been shown to reflect specific details of actual and imagined actions, and because its technical characteristics should readily support robust and chronic implementations of BCI systems in humans. This review provides general perspectives on the ECoG platform; describes the different electrophysiological features that can be detected in ECoG; elaborates on the signal acquisition issues, protocols, and online performance of ECoG-based BCI studies to date; presents important limitations of current ECoG studies; discusses opportunities for further research; and finally presents a vision for eventual clinical implementation. In summary, the studies presented to date strongly encourage further research using the ECoG platform for basic neuroscientific research, as well as for translational neuroprosthetic applications.

10aBrain-computer interface (BCI)10abrain-machine interface (BMI)10aelectrocorticography (ECoG)1 aSchalk, Gerwin1 aLeuthardt, E C uhttp://www.ncbi.nlm.nih.gov/pubmed/2227379600386nas a2200109 4500008004100000245004200041210004200083260001500125520003400140100001900174856008300193 2011 eng d00aCommunicating Directly from the Brain0 aCommunicating Directly from the Brain c10/19/20113 aEmTech Conference, MIT Campus1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/communicating-directly-brain07041nas a2200313 4500008004100000022001400041245008200055210006900137260001200206300001100218490000600229520615100235653002806386653001006414653001806424653002706442653002106469653003106490653001106521653002406532653001306556653002806569100001906597700001506616700001306631700001606644700001906660856004806679 2011 eng d a1741-255200aCurrent Trends in Hardware and Software for Brain-Computer Interfaces (BCIs).0 aCurrent Trends in Hardware and Software for BrainComputer Interf c04/2011 a0250010 v83 a

brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the developmentof certification, dissemination and reimbursement procedures.

10aBiofeedback, Psychology10aBrain10aBrain Mapping10aElectroencephalography10aEquipment Design10aEquipment Failure Analysis10aHumans10aMan-Machine Systems10aSoftware10aUser-Computer Interface1 aBrunner, Peter1 aBianchi, L1 aGuger, C1 aCincotti, F1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2143653602268nas a2200409 4500008004100000022001400041245011100055210006900166260001200235300001100247490000600258520108700264653001501351653001001366653001001376653001801386653002001404653003601424653003701460653003201497653002601529653002701555653001301582653001101595653002601606653001101632653000901643653001601652653001301668653002201681653002801703100001801731700002301749700001901772700001901791856004801810 2011 eng d a1741-255200aDecoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans.0 aDecoding vowels and consonants in spoken and imagined words usin c08/2011 a0460280 v83 a

Several stories in the popular media have speculated that it may be possible to infer from the brain which word a person is speaking or even thinking. While recent studies have demonstrated that brain signals can give detailed information about actual and imagined actions, such as different types of limb movements or spoken words, concrete experimental evidence for the possibility to 'read the mind', i.e. to interpret internally-generated speech, has been scarce. In this study, we found that it is possible to use signals recorded from the surface of the brain (electrocorticography) to discriminate the vowels and consonants embedded in spoken and in imagined words, and we defined the cortical areas that held the most information about discrimination of vowels and consonants. The results shed light on the distinct mechanisms associated with production of vowels and consonants, and could provide the basis for brain-based communication using imagined speech.

10aAdolescent10aAdult10aBrain10aBrain Mapping10aCerebral Cortex10aCommunication Aids for Disabled10aData Interpretation, Statistical10aDiscrimination (Psychology)10aElectrodes, Implanted10aElectroencephalography10aEpilepsy10aFemale10aFunctional Laterality10aHumans10aMale10aMiddle Aged10aMovement10aSpeech Perception10aUser-Computer Interface1 aPei, Xiao-Mei1 aBarbour, Dennis, L1 aLeuthardt, E C1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2175036901678nas a2200121 4500008004100000245008200041210006900123260001200192520120300204100002001407700001901427856011001446 2011 eng d00aDefense-related insights and solutions from neuroscience and neuroengineering0 aDefenserelated insights and solutions from neuroscience and neur c06/20113 aCommunication of intent usually requires motor function, which can be limiting during military missions. Determining a soldier's intent from brain signals rather than using muscles would have numerous applications for tactical combat. Brain-computer interfaces (BCIs) translate brain signals into machine readable form and could optimize a soldier's interaction with the surrounding environment. However, current BCI devices have largely remained laboratory curiosities, because current techniques either require extended training or do not have the requisite signal fidelity, because they are highly invasive and thus not safe or practical for use in humans, or because they rely on equipment (such as magnetic resonance imaging scanners) that do not allow for real-time applications and/or field deployment. The objective of our research program is to create a prototype of a system for communication and monitoring of orientation that uses brain signals to provide, in real time, an accurate assessment of the users intentional focus and imagined speech. We expect that our efforts will provide a prototype of the first intuitive brain-based communication and orientation system for human use. 1 aGunduz, Aysegul1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/defense-related-insights-and-solutions-neuroscience-and01636nas a2200121 4500008004100000245008300041210006900124260001200193520120100205100002001406700001901426856006901445 2011 eng d00aDefense-related insights and solutions from neuroscience and neuroengineering.0 aDefenserelated insights and solutions from neuroscience and neur c06/20113 aCommunication of intent usually requires motor function, which can be limiting during military missions. Determining a soldier's intent from brain signals rather than using muscles would have numerous applications for tactical combat. Brain-computer interfaces (BCIs) translate brain signals into machine readable form and could optimize a soldier's interaction with the surrounding environment. However, current BCI devices have largely remained laboratory curiosities, because current techniques either require extended training or do not have the requisite signal fidelity, because they are highly invasive and thus not safe or practical for use in humans, or because they rely on equipment (such as magnetic resonance imaging scanners) that do not allow for real-time applications and/or field deployment. The objective of our research program is to create a prototype of a system for communication and monitoring of orientation that uses brain signals to provide, in real time, an accurate assessment of the users intentional focus and imagined speech. We expect that our efforts will provide a prototype of the first intuitive brain-based communication and orientation system for human use.1 aGunduz, Aysegul1 aSchalk, Gerwin uhttp://spie.org/Publications/Proceedings/Paper/10.1117/12.88818900487nas a2200121 4500008004100000245009000041210006900131260001200200100001300212700001900225700002100244856010000265 2011 eng d00aEditorial: Current Trends in Brain-Computer Interface (BCI) Research and Development.0 aEditorial Current Trends in BrainComputer Interface BCI Research c01/20111 aNam, C S1 aSchalk, Gerwin1 aMoore-Jackson, M uhttp://www.tandfonline.com/doi/abs/10.1080/10447318.2011.535748?journalCode=hihc20#.VYwf82AxI4g00501nas a2200109 4500008004100000245005900041210005800100260001500158520009800173100001900271856010100290 2011 eng d00aElectrocorticography: A New Window into Brain Function0 aElectrocorticography A New Window into Brain Function c02/22/20113 aDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Boston, MA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/electrocorticography-new-window-brain-function00506nas a2200109 4500008004100000245006100041210006100102260001500163520009100178100001900269856010800288 2011 eng d00aExciting Adventures in Neuroscience and Neuroengineering0 aExciting Adventures in Neuroscience and Neuroengineering c04/06/20113 aDepartment of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/exciting-adventures-neuroscience-and-neuroengineering00497nas a2200109 4500008004100000245006100041210006100102260001500163520008000178100001900258856011000277 2011 eng d00aExciting Adventures in Neuroscience and Neuroengineering0 aExciting Adventures in Neuroscience and Neuroengineering c05/23/20113 aInstitute for Knowledge Discovery, Graz Technical University, Graz, Austria1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/exciting-adventures-neuroscience-and-neuroengineering-000507nas a2200109 4500008004100000245006100041210006100102260001500163520009000178100001900268856011000287 2011 eng d00aExciting Adventures in Neuroscience and Neuroengineering0 aExciting Adventures in Neuroscience and Neuroengineering c06/20/20113 aElectrical and Computer Engineering Department, University of Houston, Houston, Texas1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/exciting-adventures-neuroscience-and-neuroengineering-100405nas a2200109 4500008004100000245002800041210002800069260001500097520008900112100001900201856007500220 2011 eng d00aIntroduction to BCI20000 aIntroduction to BCI2000 c05/18/20113 a8th BCI2000 Workshop, University Medical Center, Utrecht, The Netherlands1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/introduction-bci200002535nas a2200277 4500008004100000022001400041245009300055210006900148260001200217300000700229490000600236520172900242653002101971653002501992653001402017653001902031653002902050100002002079700001902099700001602118700001902134700001802153700001902171700001902190856004802209 2011 eng d a1662-516100aNeural Correlates of Covert Attention in Electrocorticographic (ECoG) Signals in Humans.0 aNeural Correlates of Covert Attention in Electrocorticographic E c09/2011 a890 v53 a

Attention is a cognitive selection mechanism that allocates the limited processing resources of the brain to the sensory streams most relevant to our immediate goals, thereby enhancing responsiveness and behavioral performance. The underlying neural mechanisms of orienting attention are distributed across a widespread cortical network. While aspects of this network have been extensively studied, details about the electrophysiological dynamics of this network are scarce. In this study, we investigated attentional networks using electrocorticographic (ECoG) recordings from the surface of the brain, which combine broad spatial coverage with high temporal resolution, in five human subjects. ECoG was recorded when subjects covertly attended to a spatial location and responded to contrast changes in the presence of distractors in a modified Posner cueing task. ECoG amplitudes in the alpha, beta, and gamma bands identified neural changes associated with covert attention and motor preparation/execution in the different stages of the task. The results show that attentional engagement was primarily associated with ECoG activity in the visual, prefrontal, premotor, and parietal cortices. Motor preparation/execution was associated with ECoG activity in premotor/sensorimotor cortices. In summary, our results illustrate rich and distributed cortical dynamics that are associated with orienting attention and the subsequent motor preparation and execution. These findings are largely consistent with and expand on primate studies using intracortical recordings and human functional neuroimaging studies.

10acovert attention10aElectrocorticography10aintention10amotor response10avisual-spatial attention1 aGunduz, Aysegul1 aBrunner, Peter1 aDaitch, Amy1 aLeuthardt, E C1 aRitaccio, A L1 aPesaran, Bijan1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2204615303596nas a2200505 4500008004100000022001400041245010700055210006900162260001200231300001300243490000700256520217800263653002502441653001502466653001002481653002502491653001802516653001602534653002002550653002402570653002702594653001302621653002202634653001102656653001102667653000902678653001602687653002902703653002302732653002302755653001802778653002202796653001702818653001502835100002202850700001802872700002902890700002402919700002002943700001802963700002302981700001903004700001903023856004803042 2011 eng d a1529-240100aNonuniform high-gamma (60-500 Hz) power changes dissociate cognitive task and anatomy in human cortex.0 aNonuniform highgamma 60500 Hz power changes dissociate cognitive c02/2011 a2091-1000 v313 a

High-gamma-band (>60 Hz) power changes in cortical electrophysiology are a reliable indicator of focal, event-related cortical activity. Despite discoveries of oscillatory subthreshold and synchronous suprathreshold activity at the cellular level, there is an increasingly popular view that high-gamma-band amplitude changes recorded from cellular ensembles are the result of asynchronous firing activity that yields wideband and uniform power increases. Others have demonstrated independence of power changes in the low- and high-gamma bands, but to date, no studies have shown evidence of any such independence above 60 Hz. Based on nonuniformities in time-frequency analyses of electrocorticographic (ECoG) signals, we hypothesized that induced high-gamma-band (60-500 Hz) power changes are more heterogeneous than currently understood. Using single-word repetition tasks in six human subjects, we showed that functional responsiveness of different ECoG high-gamma sub-bands can discriminate cognitive task (e.g., hearing, reading, speaking) and cortical locations. Power changes in these sub-bands of the high-gamma range are consistently present within single trials and have statistically different time courses within the trial structure. Moreover, when consolidated across all subjects within three task-relevant anatomic regions (sensorimotor, Broca's area, and superior temporal gyrus), these behavior- and location-dependent power changes evidenced nonuniform trends across the population. Together, the independence and nonuniformity of power changes across a broad range of frequencies suggest that a new approach to evaluating high-gamma-band cortical activity is necessary. These findings show that in addition to time and location, frequency is another fundamental dimension of high-gamma dynamics.

10aAcoustic Stimulation10aAdolescent10aAdult10aAnalysis of Variance10aBrain Mapping10aBrain Waves10aCerebral Cortex10aCognition Disorders10aElectroencephalography10aEpilepsy10aEvoked Potentials10aFemale10aHumans10aMale10aMiddle Aged10aNeuropsychological Tests10aNonlinear Dynamics10aPhotic Stimulation10aReaction Time10aSpectrum Analysis10aTime Factors10aVocabulary1 aGaona, Charles, M1 aSharma, Mohit1 aFreudenberg, Zachary, V.1 aBreshears, Jonathan1 aBundy, David, T1 aRoland, Jarod1 aBarbour, Dennis, L1 aSchalk, Gerwin1 aLeuthardt, E C uhttp://www.ncbi.nlm.nih.gov/pubmed/2130724600543nas a2200109 4500008004100000245010600041210006900147260001500216520006500231100001900296856011800315 2011 eng d00aOpportunities for Clinical Application of Emerging Neuroscientific and Neuroengineering Understanding0 aOpportunities for Clinical Application of Emerging Neuroscientif c08/23/20113 aNeruophysiology Seminar Series, Baylor Hospital, Houston, TX1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/opportunities-clinical-application-emerging-neuroscientific-and00476nas a2200109 4500008004100000245005000041210005000091260001500141520009400156100001900250856009700269 2011 eng d00aPerspectives on ECoG Research and Application0 aPerspectives on ECoG Research and Application c11/11/20113 a3rd International Workshop on Advances in Electrocorticography, Washington, DC1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/perspectives-ecog-research-and-application02550nas a2200253 4500008004100000022001400041245009200055210006900147260001200216300000800228490000600236520180100242653002902043653002302072653002602095653001902121653002102140653002002161100001802181700001002199700002002209700001902229856004802248 2011 eng d a1662-453X00aPrior knowledge improves decoding of finger flexion from electrocorticographic signals.0 aPrior knowledge improves decoding of finger flexion from electro c11/2011 a1270 v53 a

Brain-computer interfaces (BCIs) use brain signals to convey a user's intent. Some BCI approaches begin by decoding kinematic parameters of movements from brain signals, and then proceed to using these signals, in absence of movements, to allow a user to control an output. Recent results have shown that electrocorticographic (ECoG) recordings from the surface of the brain in humans can give information about kinematic parameters (e.g., hand velocity or finger flexion). The decoding approaches in these studies usually employed classical classification/regression algorithms that derive a linear mapping between brain signals and outputs. However, they typically only incorporate little prior information about the target movement parameter. In this paper, we incorporate prior knowledge using a Bayesian decoding method, and use it to decode finger flexion from ECoG signals. Specifically, we exploit the constraints that govern finger flexion and incorporate these constraints in the construction, structure, and the probabilistic functions of the prior model of a switched non-parametric dynamic system (SNDS). Given a measurement model resulting from a traditional linear regression method, we decoded finger flexion using posterior estimation that combined the prior and measurement models. Our results show that the application of the Bayesian decoding model, which incorporates prior knowledge, improves decoding performance compared to the application of a linear regression model, which does not incorporate prior knowledge. Thus, the results presented in this paper may ultimately lead to neurally controlled hand prostheses with full fine-grained finger articulation.

10abrain-computer interface10adecoding algorithm10aelectrocorticographic10afinger flexion10amachine learning10aprior knowledge1 aWang, Zuoguan1 aJi, Q1 aMiller, John, W1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2214494402106nas a2200421 4500008004100000022001400041245009000055210006900145260001200214300001100226490000700237520091200244653001001156653001801166653001601184653003301200653002701233653001301260653001101273653001801284653002801302100001801330700002401348700001901372700002701391700002001418700002201438700002001460700002301480700001601503700002001519700001301539700001901552700002201571700002401593700001901617856004801636 2011 eng d a1525-506900aProceedings of the Second International Workshop on Advances in Electrocorticography.0 aProceedings of the Second International Workshop on Advances in  c12/2011 a641-500 v223 a

The Second International Workshop on Advances in Electrocorticography (ECoG) was convened in San Diego, CA, USA, on November 11-12, 2010. Between this meeting and the inaugural 2009 event, a much clearer picture has been emerging of cortical ECoG physiology and its relationship to local field potentials and single-cell recordings. Innovations in material engineering are advancing the goal of a stable long-term recording interface. Continued evolution of ECoG-driven brain-computer interface technology is determining innovation in neuroprosthetics. Improvements in instrumentation and statistical methodologies continue to elucidate ECoG correlates of normal human function as well as the ictal state. This proceedings document summarizes the current status of this rapidly evolving field.

10aBrain10aBrain Mapping10aBrain Waves10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aHumans10aUnited States10aUser-Computer Interface1 aRitaccio, A L1 aBoatman-Reich, Dana1 aBrunner, Peter1 aCervenka, Mackenzie, C1 aCole, Andrew, J1 aCrone, Nathan, E.1 aDuckrow, Robert1 aKorzeniewska, Anna1 aLitt, Brian1 aMiller, John, W1 aMoran, D1 aParvizi, Josef1 aViventi, Jonathan1 aWilliams, Justin, C1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2203628703409nas a2200253 4500008004100000022001400041245009700055210006900152260001200221300000600233490000600239520266600245653002902911653002502940653002802965653000902993653001203002100001903014700001803033700002203051700001503073700001903088856004803107 2011 eng d a1662-453X00aRapid Communication with a "P300" Matrix Speller Using Electrocorticographic Signals (ECoG).0 aRapid Communication with a P300 Matrix Speller Using Electrocort c02/2011 a50 v53 a

brain-computer interface (BCI) can provide a non-muscular communication channel to severely disabled people. One particular realization of a BCI is the P300 matrix speller that was originally described by Farwell and Donchin (1988). This speller uses event-related potentials (ERPs) that include the P300 ERP. All previous online studies of the P300 matrix speller used scalp-recorded electroencephalography (EEG) and were limited in their communication performance to only a few characters per minute. In our study, we investigated the feasibility of using electrocorticographic (ECoG) signals for online operation of the matrix speller, and determined associated spelling rates. We used the matrix speller that is implemented in the BCI2000 system. This speller used ECoG signals that were recorded from frontal, parietal, and occipital areas in one subject. This subject spelled a total of 444 characters in online experiments. The results showed that the subject sustained a rate of 17 characters/min (i.e., 69 bits/min), and achieved a peak rate of 22 characters/min (i.e., 113 bits/min). Detailed analysis of the results suggests that ERPs over visual areas (i.e., visual evoked potentials) contribute significantly to the performance of the matrix speller BCI system. Our results also point to potential reasons for the apparent advantages in spelling performance of ECoG compared to EEG. Thus, with additional verification in more subjects, these results may further extend the communication options for people with serious neuromuscular disabilities.

10abrain-computer interface10aElectrocorticography10aevent-related potential10aP30010aspeller1 aBrunner, Peter1 aRitaccio, A L1 aEmrich, Joseph, F1 aBischof, H1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2136935100412nas a2200109 4500008004100000245004400041210004300085260001500128520004600143100001900189856009400208 2011 eng d00aReal-Time Functional Mapping Using ECoG0 aRealTime Functional Mapping Using ECoG c11/14/20113 ag.tec ECoG/Spike Workshop, Washington, DC1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2011/real-time-functional-mapping-using-ecog03279nas a2200337 4500008004100000022001400041245011400055210006900169260001200238300001200250490000700262520231800269653001502587653001002602653001002612653001802622653002702640653001102667653001102678653000902689653001602698653004102714653002002755100001802775700001902793700002202812700001902834700002102853700001902874856004802893 2011 eng d a1095-957200aSpatiotemporal dynamics of electrocorticographic high gamma activity during overt and covert word repetition.0 aSpatiotemporal dynamics of electrocorticographic high gamma acti c02/2011 a2960-720 v543 a

Language is one of the defining abilities of humans. Many studies have characterized the neural correlates of different aspects of language processing. However, the imaging techniques typically used in these studies were limited in either their temporal or spatial resolution. Electrocorticographic (ECoG) recordings from the surface of the brain combine high spatial with high temporal resolution and thus could be a valuable tool for the study of neural correlates of language function. In this study, we defined the spatiotemporal dynamics of ECoG activity during a word repetition task in nine human subjects. ECoG was recorded while each subject overtly or covertly repeated words that were presented either visually or auditorily. ECoG amplitudes in the high gamma (HG) band confidently tracked neural changes associated with stimulus presentation and with the subject's verbal response. Overt word production was primarily associated with HG changes in the superior and middle parts of temporal lobe, Wernicke's area, the supramarginal gyrus, Broca's area, premotor cortex (PMC), primary motor cortex. Covert word production was primarily associated with HG changes in superior temporal lobe and the supramarginal gyrus. Acoustic processing from both auditory stimuli as well as the subject's own voice resulted in HG power changes in superior temporal lobe and Wernicke's area. In summary, this study represents a comprehensive characterization of overt and covert speech using electrophysiological imaging with high spatial and temporal resolution. It thereby complements the findings of previous neuroimaging studies of language and thus further adds to current understanding of word processing in humans.

10aAdolescent10aAdult10aBrain10aBrain Mapping10aElectroencephalography10aFemale10aHumans10aMale10aMiddle Aged10aSignal Processing, Computer-Assisted10aVerbal Behavior1 aPei, Xiao-Mei1 aLeuthardt, E C1 aGaona, Charles, M1 aBrunner, Peter1 aWolpaw, Jonathan1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2102978400525nas a2200145 4500008004100000022002200041245005400063210004900117260001700166300001200183100001300195700001900208700001600227856013600243 2011 eng d a978-953-307-175-600aState-of-the-Art in BCI Research: BCI Award 2010.0 aStateoftheArt in BCI Research BCI Award 2010 bInTech Press a193-2221 aGuger, C1 aSchalk, Gerwin1 aFazel, Reza uhttp://www.intechopen.com/books/recent-advances-in-brain-computer-interface-systems/state-of-the-art-in-bci-research-bci-award-201000601nas a2200205 4500008004100000022001400041245007100055210006800126260001200194300001100206490000800217653001400225653001000239653002100249653001100270653002800281100001900309700001900328856004800347 2011 eng d a1872-895200aToward a gaze-independent matrix speller brain-computer interface.0 aToward a gazeindependent matrix speller braincomputer interface c06/2011 a1063-40 v12210aAttention10aBrain10aFixation, Ocular10aHumans10aUser-Computer Interface1 aBrunner, Peter1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2118340403803nas a2200433 4500008004100000022001400041024002500055245010000080210006900180260001200249300001100261490000600272520257600278653001002854653001002864653001802874653002502892653002702917653002202944653002802966653001102994653001103005653001603016653000903032653001603041653001403057653003403071653002803105100001903133700002203152700001803174700002103192700001803213700002903231700001803260700002403278700001903302856004803321 2011 eng d a1741-2552 aNIHMSID: NIHMS48176700aUsing the electrocorticographic speech network to control a brain-computer interface in humans.0 aUsing the electrocorticographic speech network to control a brai c06/2011 a0360040 v83 a

Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive.

10aAdult10aBrain10aBrain Mapping10aComputer Peripherals10aElectroencephalography10aEvoked Potentials10aFeedback, Physiological10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aNerve Net10aSpeech Production Measurement10aUser-Computer Interface1 aLeuthardt, E C1 aGaona, Charles, M1 aSharma, Mohit1 aSzrama, Nicholas1 aRoland, Jarod1 aFreudenberg, Zachary, V.1 aSolisb, Jamie1 aBreshears, Jonathan1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2147163800411nas a2200109 4500008004100000245003000041210003000071260001500101520008600116100001900202856008000221 2010 eng d00aAdvanced BCI2000 Concepts0 aAdvanced BCI2000 Concepts c05/30/20103 a7th BCI2000 Workshop, Asilomar Conference Center, Monterey, California1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/advanced-bci2000-concepts00450nas a2200109 4500008004100000245005500041210005200096260001500148520005500163100001900218856010300237 2010 eng d00aA brain-based communication and orientation system0 abrainbased communication and orientation system c07/22/20103 a2010 US Army DDRE MURI Conference in Arlington, VA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/brain-based-communication-and-orientation-system00508nas a2200109 4500008004100000245005400041210005200095260001500147520011400162100001900276856010300295 2010 eng d00aBrain-Computer Interfaces: Prospects and Problems0 aBrainComputer Interfaces Prospects and Problems c01/27/20103 aCog Sci Issues Colloquium, Department of Cognitive Sciences, Rensselaer Polytechnic Institute, Troy, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/brain-computer-interfaces-prospects-and-problems04192nas a2200409 4500008004100000022001400041245005900055210005700114260001200171300001100183490000700194520303500201653001403236653001403250653001503264653002703279653001303306653001103319653004003330653003103370653002903401653001103430653002203441653002803463653002203491653001703513653002803530100002803558700001703586700002303603700002503626700001903651700002003670700002403690700002003714856004803734 2010 eng d a1531-824900aBrain-computer interfacing based on cognitive control.0 aBraincomputer interfacing based on cognitive control c06/2010 a809-160 v673 a

OBJECTIVE: 

Brain-computer interfaces (BCIs) translate deliberate intentions and associated changes in brain activity into action, thereby offering patients with severe paralysis an alternative means of communication with and control over their environment. Such systems are not available yet, partly due to the high performance standard that is required. A major challenge in the development of implantable BCIs is to identify cortical regions and related functions that an individual can reliably and consciously manipulate. Research predominantly focuses on the sensorimotor cortex, which can be activated by imagining motor actions. However, because this region may not provide an optimal solution to all patients, other neuronal networks need to be examined. Therefore, we investigated whether the cognitive control network can be used for BCI purposes. We also determined the feasibility of using functional magnetic resonance imaging (fMRI) for noninvasive localization of the cognitive control network.

METHODS: 

Three patients with intractable epilepsy, who were temporarily implanted with subdural grid electrodes for diagnostic purposes, attempted to gain BCI control using the electrocorticographic (ECoG) signal of the left dorsolateral prefrontal cortex (DLPFC).

RESULTS: 

All subjects quickly gained accurate BCI control by modulation of gamma-power of the left DLPFC. Prelocalization of the relevant region was performed with fMRI and was confirmed using the ECoG signals obtained during mental calculation localizer tasks.

INTERPRETATION: 

The results indicate that the cognitive control network is a suitable source of signals for BCI applications. They also demonstrate the feasibility of translating understanding about cognitive networks derived from functional neuroimaging into clinical applications.

10aCognition10aComputers10aElectrodes10aElectroencephalography10aEpilepsy10aHumans10aImage Processing, Computer-Assisted10aMagnetic Resonance Imaging10aNeuropsychological Tests10aOxygen10aPrefrontal Cortex10aPsychomotor Performance10aSpectrum Analysis10aTime Factors10aUser-Computer Interface1 aVansteensel, Mariska, J1 aHermes, Dora1 aAarnoutse, Erik, J1 aBleichner, Martin, G1 aSchalk, Gerwin1 aRijen, Peter, C1 aLeijten, Frans, S S1 aRamsey, Nick, F uhttp://www.ncbi.nlm.nih.gov/pubmed/2051794300441nas a2200133 4500008004100000022001400041245009200055210006900147260001200216300000600228490000600234100001900240856004800259 2010 eng d a1662-644300aCan Electrocorticography (ECoG) Support Robust and Powerful Brain-Computer Interfaces?.0 aCan Electrocorticography ECoG Support Robust and Powerful BrainC c06/2010 a90 v31 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2063185307936nas a2200361 4500008004100000022001400041245009600055210006900151260001200220300001100232490000800243520694900251653001507200653001007215653002807225653002007253653001007273653002507283653002407308653001107332653001107343653000907354653001607363653001907379653001607398100001607414700001907430700002207449700001707471700001707488700002107505856004807526 2010 eng d a1091-649000aCortical activity during motor execution, motor imagery, and imagery-based online feedback.0 aCortical activity during motor execution motor imagery and image c03/2010 a4430-50 v1073 a

Imagery of motor movement plays an important role in learning of complex motor skills, from learning to serve in tennis to perfecting a pirouette in ballet. What and where are the neural substrates that underlie motor imagery-based learning? We measured electrocorticographic cortical surface potentials in eight human subjects during overt action and kinesthetic imagery of the same movement, focusing on power in "high frequency" (76-100 Hz) and "low frequency" (8-32 Hz) ranges. We quantitatively establish that the spatial distribution of local neuronal population activity during motor imagery mimics the spatial distribution of activity during actual motor movement. By comparing responses to electrocortical stimulation with imagery-induced cortical surface activity, we demonstrate the role of primary motor areas in movement imagery. The magnitude of imagery-induced cortical activity change was approximately 25% of that associated with actual movement. However, when subjects learned to use this imagery to control a computer cursor in a simple feedback task, the imagery-induced activity change was significantly augmented, even exceeding that of overt movement.

10aAdolescent10aAdult10aBiofeedback, Psychology10aCerebral Cortex10aChild10aElectric Stimulation10aElectrocardiography10aFemale10aHumans10aMale10aMiddle Aged10aMotor Activity10aYoung Adult1 aMiller, K J1 aSchalk, Gerwin1 aFetz, Eberhard, E1 aNijs, Marcel1 aOjemann, J G1 aRao, Rajesh, P N uhttp://www.ncbi.nlm.nih.gov/pubmed/2016008401885nas a2200133 4500008004100000245009400041210006900135520136000204100001801564700001001582700001901592700001901611856012101630 2010 eng d00aDecoding finger flexion from electrocorticographic signals using sparse Gaussian process.0 aDecoding finger flexion from electrocorticographic signals using3 aA brain-computer interface (BCI) creates a direct communication pathway between the brain and an external device, and can thereby restore function in people with severe motor disabilities. A core component in a BCI system is the decoding algorithm that translates brain signals into action commands of an output device. Most of current decoding algorithms are based on linear models (e.g., derived using linear regression) that may have important shortcomings. The use of nonlinear models (e.g., neural networks) could overcome some of these shortcomings, but has difficulties with high dimensional feature spaces. Here we propose another decoding algorithm that is based on the sparse gaussian process with pseudo-inputs (SPGP). As a nonparametric method, it can model more complex relationships compared to linear methods. As a kernel method, it can readily deal with high dimensional feature space. The evaluations shown in this paper demonstrate that SPGP can decode the flexion of finger movements from electrocorticographic (ECoG) signals more accurately than a previously described algorithm that used a linear model. In addition, by formulating problems in the bayesian probabilistic framework, SPGP can provide estimation of the prediction uncertainty. Furthermore, the trained SPGP offers a very effective way for identifying important features.1 aWang, Zuoguan1 aJi, Q1 aMiller, Kai, J1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/decoding-finger-flexion-electrocorticographic-signals-using-sparse02830nas a2200337 4500008004100000022001400041245004900055210004500104260001200149300001100161490000600172520196200178653001002140653003502150653001802185653001102203653001102214653000902225653001602234653002502250653002302275653002802298653001602326100001902342700001302361700001502374700002102389700001502410700001902425856004802444 2010 eng d a1741-255200aDoes the 'P300' speller depend on eye gaze?.0 aDoes the P300 speller depend on eye gaze c10/2010 a0560130 v73 a

Many people affected by debilitating neuromuscular disorders such as amyotrophic lateral sclerosis, brainstem stroke or spinal cord injury are impaired in their ability to, or are even unable to, communicate. A brain-computer interface (BCI) uses brain signals, rather than muscles, to re-establish communication with the outside world. One particular BCI approach is the so-called 'P300 matrix speller' that was first described by Farwell and Donchin (1988 Electroencephalogr. Clin. Neurophysiol. 70 510-23). It has been widely assumed that this method does not depend on the ability to focus on the desired character, because it was thought that it relies primarily on the P300-evoked potential and minimally, if at all, on other EEG features such as the visual-evoked potential (VEP). This issue is highly relevant for the clinical application of this BCI method, because eye movements may be impaired or lost in the relevant user population. This study investigated the extent to which the performance in a 'P300' speller BCI depends on eye gaze. We evaluated the performance of 17 healthy subjects using a 'P300' matrix speller under two conditions. Under one condition ('letter'), the subjects focused their eye gaze on the intended letter, while under the second condition ('center'), the subjects focused their eye gaze on a fixation cross that was located in the center of the matrix. The results show that the performance of the 'P300' matrix speller in normal subjects depends in considerable measure on gaze direction. They thereby disprove a widespread assumption in BCI research, and suggest that this BCI might function more effectively for people who retain some eye-movement control. The applicability of these findings to people with severe neuromuscular disabilities (particularly in eye-movements) remains to be determined.

10aAdult10aEvent-Related Potentials, P30010aEye Movements10aFemale10aHumans10aMale10aMiddle Aged10aModels, Neurological10aPhotic Stimulation10aUser-Computer Interface10aYoung Adult1 aBrunner, Peter1 aJoshi, S1 aBriskin, S1 aWolpaw, Jonathan1 aBischof, H1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2085892404454nas a2200433 4500008004100000022001400041245010500055210006900160260001200229300001100241490000700252520325900259653002503518653001503543653001003558653001803568653002003586653002803606653002703634653001303661653001103674653001103685653000903696653002203705653001603727653002303743653001103766653002003777653001603797100001603813700002303829700001903852700001803871700001803889700002403907700002203931700001903953856004803972 2010 eng d a1524-404000aElectrocorticographic frequency alteration mapping for extraoperative localization of speech cortex.0 aElectrocorticographic frequency alteration mapping for extraoper c02/2010 aE407-90 v663 a

OBJECTIVE: 

Electrocortical stimulation (ECS) has long been established for delineating eloquent cortex in extraoperative mapping. However, ECS is still coarse and inefficient in delineating regions of functional cortex and can be hampered by afterdischarges. Given these constraints, an adjunct approach to defining motor cortex is the use of electrocorticographic (ECoG) signal changes associated with active regions of cortex. The broad range of frequency oscillations are categorized into 2 main groups with respect to sensorimotor cortex: low-frequency bands (LFBs) and high-frequency bands (HFBs). The LFBs tend to show a power reduction, whereas the HFBs show power increases with cortical activation. These power changes associated with activated cortex could potentially provide a powerful tool in delineating areas of speech cortex. We explore ECoG signal alterations as they occur with activated region of speech cortex and its potential in clinical brain mapping applications.

METHODS: 

We evaluated 7 patients who underwent invasive monitoring for seizure localization. Each had extraoperative ECS mapping to identify speech cortex. Additionally, all subjects performed overt speech tasks with an auditory or a visual cue to identify associated frequency power changes in regard to location and degree of concordance with ECS results.

RESULTS: 

Electrocorticographic frequency alteration mapping (EFAM) had an 83.9% sensitivity and a 40.4% specificity in identifying any language site when considering both frequency bands and both stimulus cues. Electrocorticographic frequency alteration mapping was more sensitive in identifying the Wernicke area (100%) than the Broca area (72.2%). The HFB is uniquely suited to identifying the Wernicke area, whereas a combination of the HFB and LFB is important for Broca localization.

CONCLUSION: 

The concordance between stimulation and spectral power changes demonstrates the possible utility of EFAM as an adjunct method to improve the efficiency and resolution of identifying speech cortex.

10aAcoustic Stimulation10aAdolescent10aAdult10aBrain Mapping10aCerebral Cortex10aChi-Square Distribution10aElectroencephalography10aEpilepsy10aFemale10aHumans10aMale10aMass Spectrometry10aMiddle Aged10aPhotic Stimulation10aSpeech10aVerbal Behavior10aYoung Adult1 aWu, Melinda1 aWisneski, Kimberly1 aSchalk, Gerwin1 aSharma, Mohit1 aRoland, Jarod1 aBreshears, Jonathan1 aGaona, Charles, M1 aLeuthardt, E C uhttp://www.ncbi.nlm.nih.gov/pubmed/2008711100486nas a2200109 4500008004100000245004700041210004700088260001500135520011100150100001900261856009600280 2010 eng d00aEmerging Opportunities in Neuroengineering0 aEmerging Opportunities in Neuroengineering c10/01/20103 aDepartment of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/emerging-opportunities-neuroengineering-000473nas a2200109 4500008004100000245004700041210004700088260001500135520010000150100001900250856009400269 2010 eng d00aEmerging Opportunities in Neuroengineering0 aEmerging Opportunities in Neuroengineering c08/27/20103 aGraduate School of Biomedical Engineering, The University of New South Wales, Sydney, Australia1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/emerging-opportunities-neuroengineering00547nas a2200109 4500008004100000245008000041210006900121260001500190520008900205100001900294856012400313 2010 eng d00aEncoding of Perception and Cognition in Human Electrocorticographic Signals0 aEncoding of Perception and Cognition in Human Electrocorticograp c09/30/20103 aKeynote Address, Bernstein Conference on Computational Neuroscience, Berlin, Germany1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/encoding-perception-and-cognition-human-electrocorticographic-signals00481nas a2200109 4500008004100000245005400041210005400095260001500149520008500164100001900249856010300268 2010 eng d00aExciting Directions in Human Electrocorticography0 aExciting Directions in Human Electrocorticography c05/28/20103 aUniversity of California San Francisco Medical School, San Francisco, California1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/exciting-directions-human-electrocorticography-000517nas a2200109 4500008004100000245005400041210005400095260001500149520012300164100001900287856010100306 2010 eng d00aExciting Directions in Human Electrocorticography0 aExciting Directions in Human Electrocorticography c01/20/20103 aSmall Scale Systems and Integration and Packaging Center's Seminar Series, Binghamton University, Binghamton, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/exciting-directions-human-electrocorticography00545nas a2200109 4500008004100000245006100041210006100102260001500163520013000178100001900308856010800327 2010 eng d00aExciting Directions in Neuroscience and Neuroengineering0 aExciting Directions in Neuroscience and Neuroengineering c12/13/20103 aDepartment of Physical Therapy and Human Movement Sciences, Northwestern University, Feinberg School of Medicine, Chicago, IL1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/exciting-directions-neuroscience-and-neuroengineering00530nas a2200109 4500008004100000245009700041210006900138260001500207520005500222100001900277856012400296 2010 eng d00aInferring Detailed Aspects of COgnition Using Electrocorticographic (ECoG) Signals in Humans0 aInferring Detailed Aspects of COgnition Using Electrocorticograp c09/17/20103 aSeattle Children's Research Institute, Seattle, WA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/inferring-detailed-aspects-cognition-using-electrocorticographic-ecog00402nas a2200109 4500008004100000245002800041210002800069260001500097520008600112100001900198856007500217 2010 eng d00aIntroduction to BCI20000 aIntroduction to BCI2000 c05/30/20103 a7th BCI2000 Workshop, Asilomar Conference Center, Monterey, California1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/introduction-bci200000567nas a2200109 4500008004100000245010800041210006900149260001500218520008600233100001900319856011900338 2010 eng d00aNeuroscience and Brain-Computer Interface Research Using Signals Recorded from the Surface of the Brain0 aNeuroscience and BrainComputer Interface Research Using Signals  c02/22/20103 aHudson Valley-Berkshire Chapter of the Society for Neuroscience, Albany, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/neuroscience-and-brain-computer-interface-research-using-signals00517nas a2200109 4500008004100000245007200041210006900113260001500182520007200197100001900269856011900288 2010 eng d00aNovel Methods and Applications in Brain-Computer Interface Research0 aNovel Methods and Applications in BrainComputer Interface Resear c07/21/20103 aU.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen MD1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/novel-methods-and-applications-brain-computer-interface-research03481nas a2200289 4500008004100000022001400041245009200055210006900147260001200216300001000228490000700238520264000245653001802885653002002903653002002923653001502943653002502958653002702983653001103010653002703021100001803048700001903066700002003085700001903105700001903124856004803143 2010 eng d a1525-506900aPassive real-time identification of speech and motor cortex during an awake craniotomy.0 aPassive realtime identification of speech and motor cortex durin c05/2010 a123-80 v183 a

Precise localization of eloquent cortex is a clinical necessity prior to surgical resections adjacent to speech or motor cortex. In the intraoperative setting, this traditionally requires inducing temporary lesions by direct electrocortical stimulation (DECS). In an attempt to increase efficiency and potentially reduce the amount of necessary stimulation, we used a passive mapping procedure in the setting of an awake craniotomy for tumor in two patients resection. We recorded electrocorticographic (ECoG) signals from exposed cortex while patients performed simple cue-directed motor and speech tasks. SIGFRIED, a procedure for real-time event detection, was used to identify areas of cortical activation by detecting task-related modulations in the ECoG high gamma band. SIGFRIED's real-time output quickly localized motor and speech areas of cortex similar to those identified by DECS. In conclusion, real-time passive identification of cortical function using SIGFRIED may serve as a useful adjunct to cortical stimulation mapping in the intraoperative setting.

10aBrain Mapping10aBrain Neoplasms10aCerebral Cortex10aCraniotomy10aElectric Stimulation10aElectroencephalography10aHumans10aNeurologic Examination1 aRoland, Jarod1 aBrunner, Peter1 aJohnston, James1 aSchalk, Gerwin1 aLeuthardt, E C uhttp://www.ncbi.nlm.nih.gov/pubmed/2047874500487nam a2200133 4500008004100000022002200041245006600063210006200129260005000191300000800241100001900249700002300268856006200291 2010 eng d a978-1-84996-091-500aA Practical Guide to Brain-Computer Interfacing with BCI2000.0 aPractical Guide to BrainComputer Interfacing with BCI2000 bSpringer London Dordrecht Heidelberg New York a2881 aSchalk, Gerwin1 aMellinger, Jürgen uhttp://link.springer.com/book/10.1007%2F978-1-84996-092-201606nas a2200301 4500008004100000022001400041245007000055210006600125260001200191300001200203490000700215520071500222653001000937653002100947653002700968653002200995653001101017653002501028653003101053653004101084653001701125653002801142100002001170700002301190700001901213700002401232856004801256 2010 eng d a1558-253100aA procedure for measuring latencies in brain-computer interfaces.0 aprocedure for measuring latencies in braincomputer interfaces c06/2010 a1785-970 v573 a

Brain-computer interface (BCI) systems must process neural signals with consistent timing in order to support adequate system performance. Thus, it is important to have the capability to determine whether a particular BCI configuration (i.e., hardware and software) provides adequate timing performance for a particular experiment. This report presents a method of measuring and quantifying different aspects of system timing in several typical BCI experiments across a range of settings, and presents comprehensive measures of expected overall system latency for each experimental configuration.

10aBrain10aComputer Systems10aElectroencephalography10aEvoked Potentials10aHumans10aModels, Neurological10aReproducibility of Results10aSignal Processing, Computer-Assisted10aTime Factors10aUser-Computer Interface1 aWilson, Adam, J1 aMellinger, Jürgen1 aSchalk, Gerwin1 aWilliams, Justin, C uhttp://www.ncbi.nlm.nih.gov/pubmed/2040378101656nas a2200337 4500008004100000022001400041245008900055210006900144260001200213300001100225490000700236520066900243653001000912653001800922653003300940653002700973653001101000653003001011653001301041653003601054100001801090700001901108700002701127700002201154700001301176700001901189700002301208700002001231700001901251856004801270 2010 eng d a1525-506900aProceedings of the first international workshop on advances in electrocorticography.0 aProceedings of the first international workshop on advances in e c10/2010 a204-150 v193 a

In October 2009, a group of neurologists, neurosurgeons, computational neuroscientists, and engineers congregated to present novel developments transforming human electrocorticography (ECoG) beyond its established relevance in clinical epileptology. The contents of the proceedings advanced the role of ECoG in seizure detection and prediction, neurobehavioral research, functional mapping, and brain-computer interface technology. The meeting established the foundation for future work on the methodology and application of surface brain recordings.

10aBrain10aBrain Mapping10aDiagnosis, Computer-Assisted10aElectroencephalography10aHumans10aInternational Cooperation10aSeizures10aSignal Detection, Psychological1 aRitaccio, A L1 aBrunner, Peter1 aCervenka, Mackenzie, C1 aCrone, Nathan, E.1 aGuger, C1 aLeuthardt, E C1 aOostenveld, Robert1 aStacey, William1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2088938400511nas a2200109 4500008004100000245006900041210006800110260001500178520007000193100001900263856011900282 2010 eng d00aReal-Time Functional Mapping Using Electrocorticographic Signals0 aRealTime Functional Mapping Using Electrocorticographic Signals c09/17/20103 aDepartment of Neurology, Seattle Children's Hospital, Seattle, WA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/real-time-functional-mapping-using-electrocorticographic-signals00436nas a2200109 4500008004100000245003600041210003500077260001500112520009400127100001900221856008600240 2010 eng d00aToward Brain-Computer Symbiosis0 aToward BrainComputer Symbiosis c01/07/20103 aKeynote Address, X-Prize Workshop on Brain-Computer Interfaces, MIT Campus, Cambridge, MA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/toward-brain-computer-symbiosis01367nas a2200169 4500008004100000022001900041245004900060210004700109260002000156300001200176520087600188100002001064700001901084700001501103700001101118856006801129 2010 eng d a978-184996271100aUsing BCI2000 for HCI-Centered BCI Research.0 aUsing BCI2000 for HCICentered BCI Research bSpringer London a261-2743 aBCI2000 is a general-purpose software suite designed for brain-computer interface (BCI) and related research. BCI2000 has been in development since 2000 and is currently used in close to 500 laboratories around the world. BCI2000 can provide stimulus presentation while simultaneously recording brain signals and subject responses from a number of data acquisition and input devices, respectively. Furthermore, BCI2000 provides a number of services (such as a generic data format that can accommodate any hardware or experimental setup) that can greatly facilitate research. In summary, BCI2000 is ideally suited to support investigations in the area of human-computer interfaces (HCI), in particular those that include recording and processing of brain signals. This chapter provides an overview of the BCI2000 system, and gives examples of its utility for HCI research.1 aWilson, Adam, J1 aSchalk, Gerwin1 aNijholt, A1 aTan, D uhttp://link.springer.com/chapter/10.1007%2F978-1-84996-272-8_1501307nas a2200181 4500008004100000022001900041245003500060210003400095260003100129300001200160520078800172100002300960700001900983700002301002700002401025700002501049856005101074 2010 eng d a978-364202090200aUsing BCI2000 in BCI Research.0 aUsing BCI2000 in BCI Research bSpringer Berlin Heidelberg a259-2803 a

BCI2000 is a general-purpose system for brain–computer interface (BCI) research. It can also be used for data acquisition, stimulus presentation, and brain monitoring applications [18,27]. The mission of the BCI2000 project is to facilitate research and applications in these areas. BCI2000 has been in development since 2000 in a collaboration between the Wadsworth Center of the New York State Department of Health in Albany, New York, and the Institute of Medical Psychology and Behavioral Neurobiology at the University of Tübingen, Germany. Many other individuals at different institutions world-wide have contributed to this project.

1 aMellinger, Jürgen1 aSchalk, Gerwin1 aGraimann, Bernhard1 aPfurtscheller, Gert1 aAllison, Brendan, Z. uhttp://dx.doi.org/10.1007/978-3-642-02091-9_1500534nas a2200109 4500008004100000245007300041210006900114260001500183520008700198100001900285856012000304 2010 eng d00aUsing Neuroscience and Neuroengineering to Augment Human Performance0 aUsing Neuroscience and Neuroengineering to Augment Human Perform c12/01/20103 aTopical Panel on Neuroscience, 27th Army Science Conference Orlando, FL1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2010/using-neuroscience-and-neuroengineering-augment-human-performance00554nas a2200109 4500008004100000245007800041210006900119260001500188520009800203100001900301856012400320 2009 eng d00aBCI2000: A General-Purpose BCI System and its Application to ECoG Signals0 aBCI2000 A GeneralPurpose BCI System and its Application to ECoG  c10/17/20093 ag.tec Brain-Computer Interface Workshop, Society for Neuroscience Annual Meeting, Chicago, IL1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/bci2000-general-purpose-bci-system-and-its-application-ecog-signals-000539nas a2200109 4500008004100000245007800041210006900119260001500188520008500203100001900288856012200307 2009 eng d00aBCI2000: A General-Purpose BCI System and its Application to ECoG Signals0 aBCI2000 A GeneralPurpose BCI System and its Application to ECoG  c07/19/20093 aTutorial T02: Brain-Computer Interface, HCI International, San Diego, California1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/bci2000-general-purpose-bci-system-and-its-application-ecog-signals00399nas a2200109 4500008004100000245002600041210002200067260001500089520009400104100001900198856007200217 2009 eng d00aThe BCI2000 Framework0 aBCI2000 Framework c10/01/20093 a5th BCI2000 Workshop, The Sagamore Conference Center, Bolton Landing, new York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/bci2000-framework00477nas a2200109 4500008004100000245003100041210003000072260001500102520014800117100001900265856008300284 2009 eng d00aBrain-Computer Interaction0 aBrainComputer Interaction c07/22/20093 aSession Applications and Challenges in Neurally-Driven System Interfaces, Intl. Conference on Augmented Cognition, San Diego, California1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/brain-computer-interaction-001207nas a2200181 4500008004100000020002200041245003200063210003000095260001900125520069300144653000800837653002900845653002300874653002200897100001900919700001900938856006800957 2009 eng d a978-3-642-02811-300aBrain-Computer Interaction.0 aBrainComputer Interaction bSpringerc20093 a

Detection and automated interpretation of attention-related or intention-related brain activity carries significant promise for many military and civilian applications. This interpretation of brain activity could provide information about a person’s intended movements, imagined movements, or attentional focus, and thus could be valuable for optimizing or replacing traditional motor-based communication between a person and a computer or other output devices. We describe here the objective and preliminary results of our studies in this area.

10aBCI10abrain-computer interface10aneural engineering10aneural prosthesis1 aBrunner, Peter1 aSchalk, Gerwin uhttp://link.springer.com/chapter/10.1007%2F978-3-642-02812-0_8100566nas a2200109 4500008004100000245006500041210006200106260001500168520014100183100001900324856011300343 2009 eng d00aBrain-Computer Interfacing Using Electrocorticography (ECoG)0 aBrainComputer Interfacing Using Electrocorticography ECoG c07/20/20093 aSwartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/brain-computer-interfacing-using-electrocorticography-ecog00495nas a2200109 4500008004100000245006500041210006200106260001500168520006800183100001900251856011500270 2009 eng d00aBrain-Computer Interfacing Using Electrocorticography (ECoG)0 aBrainComputer Interfacing Using Electrocorticography ECoG c12/05/20093 aBeijing BCI20009 Symposium, Tsinghua University, Beijing, China1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/brain-computer-interfacing-using-electrocorticography-ecog-000576nas a2200109 4500008004100000245006000041210005900101260001500160520016000175100001900335856011200354 2009 eng d00aBrain-COmputer Interfacing Using P300 Evoked Potentials0 aBrainCOmputer Interfacing Using P300 Evoked Potentials c07/21/20093 aGuest lecture in course Brain-Computer Interface Systems, Department of Cognitive Sciences, University of California San Diego, La Jolla, California1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/brain-computer-interfacing-using-p300-evoked-potentials-000570nas a2200109 4500008004100000245006000041210005900101260001000160520016100170100001900331856011000350 2009 eng d00aBrain-Computer Interfacing Using P300 Evoked Potentials0 aBrainComputer Interfacing Using P300 Evoked Potentials c03/253 aGuest lecture in course Brain-Computer Interfaces, Departments of Neurosurgery/Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/brain-computer-interfacing-using-p300-evoked-potentials02146nas a2200397 4500008004100000022001400041245009000055210006900145260001200214300001100226490000600237520112800243653001501371653001001386653001701396653001001413653002101423653001301444653001101457653001201468653001101480653000901491653002001500653001601520653001901536653000901555653001001564653001701574653001601591100001601607700002001623700001701643700002101660700001901681856004801700 2009 eng d a1741-255200aDecoding flexion of individual fingers using electrocorticographic signals in humans.0 aDecoding flexion of individual fingers using electrocorticograph c12/2009 a0660010 v63 a

Brain signals can provide the basis for a non-muscular communication and control system, a brain-computer interface (BCI), for people with motor disabilities. A common approach to creating BCI devices is to decode kinematic parameters of movements using signals recorded by intracortical microelectrodes. Recent studies have shown that kinematic parameters of hand movements can also be accurately decoded from signals recorded by electrodes placed on the surface of the brain (electrocorticography (ECoG)). In the present study, we extend these results by demonstrating that it is also possible to decode the time course of the flexion of individual fingers using ECoG signals in humans, and by showing that these flexion time courses are highly specific to the moving finger. These results provide additional support for the hypothesis that ECoG could be the basis for powerful clinically practical BCI systems, and also indicate that ECoG is useful for studying cortical dynamics related to motor function.

10aAdolescent10aAdult10aBiomechanics10aBrain10aElectrodiagnosis10aEpilepsy10aFemale10aFingers10aHumans10aMale10aMicroelectrodes10aMiddle Aged10aMotor Activity10aRest10aThumb10aTime Factors10aYoung Adult1 aKubánek, J1 aMiller, John, W1 aOjemann, J G1 aWolpaw, Jonathan1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/1979423700495nas a2200109 4500008004100000245005900041210005900100260001500159520008900174100001900263856010300282 2009 eng d00aDetecting Detailed Aspects of Behavior in ECoG Signals0 aDetecting Detailed Aspects of Behavior in ECoG Signals c10/02/20093 aInternational Workshop on Advances in Electrocorticography, Bolton Landing, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/detecting-detailed-aspects-behavior-ecog-signals01983nas a2200373 4500008004100000022001400041245011800055210006900173260000900242300001100251490000900262520077500271653001501046653002401061653003001085653001101115653000901126653003501135653003201170653002301202653003101225653003201256653002801288653001801316100002001334700001701354700001901371700002001390700002401410700001701434700001701451700002101468856012001489 2009 eng d a1557-170X00aDetection of spontaneous class-specific visual stimuli with high temporal accuracy in human electrocorticography.0 aDetection of spontaneous classspecific visual stimuli with high  c2009 a6465-80 v20093 aMost brain-computer interface classification experiments from electrical potential recordings have been focused on the identification of classes of stimuli or behavior where the timing of experimental parameters is known or pre-designated. Real world experience, however, is spontaneous, and to this end we describe an experiment predicting the occurrence, timing, and types of visual stimuli perceived by a human subject from electrocorticographic recordings. All 300 of 300 presented stimuli were correctly detected, with a temporal precision of order 20 ms. The type of stimulus (face/house) was correctly identified in 95% of these cases. There were approximately 20 false alarm events, corresponding to a late 2nd neuronal response to a previously identified event.10aAlgorithms10aElectrocardiography10aEvoked Potentials, Visual10aHumans10aMale10aPattern Recognition, Automated10aPattern Recognition, Visual10aPhotic Stimulation10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface10aVisual Cortex1 aMiller, John, W1 aHermes, Dora1 aSchalk, Gerwin1 aRamsey, Nick, F1 aJagadeesh, Bharathi1 aNijs, Marcel1 aOjemann, J G1 aRao, Rajesh, P N uhttps://www.neurotechcenter.org/publications/2009/detection-spontaneous-class-specific-visual-stimuli-high-temporal00573nas a2200109 4500008004100000245006800041210006600109260001500175520014600190100001900336856010800355 2009 eng d00aEEG/ECoG-based BCIs for People with Little or No Motor Function0 aEEGECoGbased BCIs for People with Little or No Motor Function c04/27/20093 aSeminar Brain-Computer Interfaces: Frontiers in Neurology and Neuroscience, American Academy of Neurology Meeting, Seattle, Washington1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/eegecog-based-bcis-people-little-or-no-motor-function00529nas a2200109 4500008004100000245005500041210005400096260001500150520012800165100001900293856010700312 2009 eng d00aEffective Brain-Computer Interfacing Using BCI20000 aEffective BrainComputer Interfacing Using BCI2000 c09/05/20093 aSession Brain-Machine Interface I, 31st Annual International IEEE EMBS Conference, Minneapolis, Minnesota1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/effective-brain-computer-interfacing-using-bci2000-001326nas a2200265 4500008004100000022001400041245005600055210005400111260000900165300001300174490000900187520048800196653001500684653001000699653002400709653002100733653003100754653001900785653003100804653003200835653004100867653002800908100001900936856010500955 2009 eng d a1557-170X00aEffective brain-computer interfacing using BCI2000.0 aEffective braincomputer interfacing using BCI2000 c2009 a5498-5010 v20093 aTo facilitate research and development in Brain-Computer Interface (BCI) research, we have been developing a general-purpose BCI system, called BCI2000, over the past nine years. This system has enjoyed a growing adoption in BCI and related areas and has been the basis for some of the most impressive studies reported to date. This paper gives an update on the status of this project by describing the principles of the BCI2000 system, its benefits, and impact on the field to date.10aAlgorithms10aBrain10aElectrocardiography10aEquipment Design10aEquipment Failure Analysis10aRehabilitation10aReproducibility of Results10aSensitivity and Specificity10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/effective-brain-computer-interfacing-using-bci200000432nas a2200109 4500008004100000245004700041210004700088260001500135520005900150100001900209856009400228 2009 eng d00aEmerging Opportunities in Neuroengineering0 aEmerging Opportunities in Neuroengineering c06/18/20093 aUniversity of Pennsylvania, Philadelphia, Pennsylvania1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/emerging-opportunities-neuroengineering00522nas a2200109 4500008004100000245009500041210006900136260001500205520005000220100001900270856012300289 2009 eng d00aEngineering the Future in Biomedicine: Using Brain Signals for Communication and Diagnosis0 aEngineering the Future in Biomedicine Using Brain Signals for Co c04/03/20093 aIEEE Schenectady Section, Niskayuna, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/engineering-future-biomedicine-using-brain-signals-communication-and02156nas a2200337 4500008004100000022001400041245008300055210006900138260001200207300000700219490000700226520120000233653001001433653002001443653001101463653002401474653001701498653001301515653002301528653002401551653002801575653001301603653004101616653002801657100001901685700001901704700001801723700001601741700001301757856004801770 2009 eng d a1092-068400aEvolution of brain-computer interfaces: going beyond classic motor physiology.0 aEvolution of braincomputer interfaces going beyond classic motor c07/2009 aE40 v273 a

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.

10aBrain10aCerebral Cortex10aHumans10aMan-Machine Systems10aMotor Cortex10aMovement10aMovement Disorders10aNeuronal Plasticity10aProstheses and Implants10aResearch10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aLeuthardt, E C1 aSchalk, Gerwin1 aRoland, Jarod1 aRouse, Adam1 aMoran, D uhttp://www.ncbi.nlm.nih.gov/pubmed/1956989200389nas a2200109 4500008004100000245002800041210002800069260001500097520007300112100001900185856007500204 2009 eng d00aIntroduction to BCI20000 aIntroduction to BCI2000 c12/06/20093 a6th BCI2000 Workshop, Tsinghua University, Beijing, China1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/introduction-bci200000461nas a2200109 4500008004100000245004500041210004500086260001500131520009400146100001900240856009200259 2009 eng d00aOverview of Available BCI2000 Components0 aOverview of Available BCI2000 Components c10/01/20093 a5th BCI2000 Workshop, The Sagamore Conference Center, Bolton Landing, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/overview-available-bci2000-components02591nas a2200433 4500008004100000022001400041245012500055210006900180260001200249300001100261490000700272520133300279653001001612653001801622653002001640653002501660653002601685653002701711653001301738653001101751653001101762653000901773653001601782653003301798653004101831653001601872100001901888700001801907700002201925700002201947700002001969700002401989700002302013700002002036700001902056700001502075700001902090856004802109 2009 eng d a1525-506900aA practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans.0 apractical procedure for realtime functional mapping of eloquent  c07/2009 a278-860 v153 a

Functional mapping of eloquent cortex is often necessary prior to invasive brain surgery, but current techniques that derive this mapping have important limitations. In this article, we demonstrate the first comprehensive evaluation of a rapid, robust, and practical mapping system that uses passive recordings of electrocorticographic signals. This mapping procedure is based on the BCI2000 and SIGFRIED technologies that we have been developing over the past several years. In our study, we evaluated 10 patients with epilepsy from four different institutions and compared the results of our procedure with the results derived using electrical cortical stimulation (ECS) mapping. The results show that our procedure derives a functional motor cortical map in only a few minutes. They also show a substantial concurrence with the results derived using ECS mapping. Specifically, compared with ECS maps, a next-neighbor evaluation showed no false negatives, and only 0.46 and 1.10% false positives for hand and tongue maps, respectively. In summary, we demonstrate the first comprehensive evaluation of a practical and robust mapping procedure that could become a new tool for planning of invasive brain surgeries.

10aAdult10aBrain Mapping10aCerebral Cortex10aElectric Stimulation10aElectrodes, Implanted10aElectroencephalography10aEpilepsy10aFemale10aHumans10aMale10aMiddle Aged10aPractice Guidelines as Topic10aSignal Processing, Computer-Assisted10aYoung Adult1 aBrunner, Peter1 aRitaccio, A L1 aLynch, Timothy, M1 aEmrich, Joseph, F1 aWilson, Adam, J1 aWilliams, Justin, C1 aAarnoutse, Erik, J1 aRamsey, Nick, F1 aLeuthardt, E C1 aBischof, H1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/1936663800548nas a2200109 4500008004100000245009100041210006900132260001500201520007800216100001900294856012500313 2009 eng d00aReal-Time Data Acquisition, Signal Processing, and Stimulus Presentation Using BCI20000 aRealTime Data Acquisition Signal Processing and Stimulus Present c10/07/20093 aWorkshop Neural Engineering in Real Time, Pittsburgh, Pennsylvania1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/real-time-data-acquisition-signal-processing-and-stimulus-presentation00552nas a2200109 4500008004100000245006900041210006800110260001500178520011100193100001900304856011900323 2009 eng d00aReal-Time Functional Mapping Using Electrocorticographic Signals0 aRealTime Functional Mapping Using Electrocorticographic Signals c11/18/20093 aClinical Neurophysiology Research Seminar, Langone Medical Center, New York University, New York, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/real-time-functional-mapping-using-electrocorticographic-signals00548nas a2200109 4500008004100000245008100041210006900122260001500191520008800206100001900294856012500313 2009 eng d00aResearch and Clinical Application of Electrocorticographic Signals in Humans0 aResearch and Clinical Application of Electrocorticographic Signa c12/01/20093 aHelen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/research-and-clinical-application-electrocorticographic-signals-humans01059nas a2200121 4500008004100000245005300041210005200094260001300146490000900159520065100168100001900819856009900838 2009 eng d00aSensor Modalities for Brain-Computer Interfacing0 aSensor Modalities for BrainComputer Interfacing bSpringer0 v56113 aMany people have neuromuscular conditions or disorders that impair the neural pathways that control muscles. Those most severely affected lose all voluntary muscle control and hence lose the ability to communicate. Brain-computer interfaces (BCIs) might be able to restore some communication or control functions for these people by creating a new communication channel – directly from the brain to an output device. Many studies over the past two decades have shown that such BCI communication is possible and that it can serve useful functions. This paper reviews the different sensor methodologies that have been explored in these studies. 1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/sensor-modalities-brain-computer-interfacing00549nas a2200109 4500008004100000245005300041210005200094260001500146520015800161100001900319856010100338 2009 eng d00aSensor Modalities for Brain-Computer Interfacing0 aSensor Modalities for BrainComputer Interfacing c07/22/20093 aSession Brain-Computer Interface (BCI); Towards Understanding Neural Bases of Human-Computer Interaction, HCI International, San Diego, California1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/sensor-modalities-brain-computer-interfacing-000524nas a2200109 4500008004100000245007200041210006900113260001500182520008300197100001900280856011500299 2009 eng d00aTechnical Basis for Real-Time Functional Mapping of Eloquent Cortex0 aTechnical Basis for RealTime Functional Mapping of Eloquent Cort c03/17/20093 aDepartment of Neurology, Weill Cornell Medical Center, New York City, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/technical-basis-real-time-functional-mapping-eloquent-cortex00546nas a2200109 4500008004100000245007700041210006900118260001500187520009600202100001900298856011900317 2009 eng d00aTheory and Application of Electrocorticographic (ECoG) Signals in Humans0 aTheory and Application of Electrocorticographic ECoG Signals in  c07/08/20093 aInvited 1.5 hour tutorial, BBCI Workshop 2009, Advances in Neurotechnology, Berlin, Germany1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/theory-and-application-electrocorticographic-ecog-signals-humans02198nas a2200229 4500008004100000022001400041245009000055210006900145260001200214520150500226653001001731653001601741653001501757653002701772653001101799653002801810100002001838700001901858700001901877700002401896856004801920 2009 eng d a1940-087X00aUsing an EEG-based brain-computer interface for virtual cursor movement with BCI2000.0 aUsing an EEGbased braincomputer interface for virtual cursor mov c07/20093 a

A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to control a computer or other device. The amplitude of the EEG signals in selected frequency bins are measured and translated into a device command, in this case the horizontal and vertical velocity of a computer cursor. First, the EEG electrodes are applied to the user s scalp using a cap to record brain activity. Next, a calibration procedure is used to find the EEG electrodes and features that the user will learn to voluntarily modulate to use the BCI. In humans, the power in the mu (8-12 Hz) and beta (18-28 Hz) frequency bands decrease in amplitude during a real or imagined movement. These changes can be detected in the EEG in real-time, and used to control a BCI ([1],[2]). Therefore, during a screening test, the user is asked to make several different imagined movements with their hands and feet to determine the unique EEG features that change with the imagined movements. The results from this calibration will show the best channels to use, which are configured so that amplitude changes in the mu and beta frequency bands move the cursor either horizontally or vertically. In this experiment, the general purpose BCI system BCI2000 is used to control signal acquisition, signal processing, and feedback to the user [3].

10aBrain10aCalibration10aElectrodes10aElectroencephalography10aHumans10aUser-Computer Interface1 aWilson, Adam, J1 aSchalk, Gerwin1 aWalton, Léo M1 aWilliams, Justin, C uhttp://www.ncbi.nlm.nih.gov/pubmed/1964147900522nas a2200109 4500008004100000245008900041210006900130260001500199520005900214100001900273856012000292 2009 eng d00aUsing Electrocorticographic (ECoG) Signals in Humans for Communication and Diagnosis0 aUsing Electrocorticographic ECoG Signals in Humans for Communica c03/25/20093 aUniversity of Pennsylvania, Philadelphia, Pennsylvania1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/using-electrocorticographic-ecog-signals-humans-communication-and00505nas a2200109 4500008004100000245006900041210006900110260001500179520007000194100001900264856011200283 2009 eng d00aUsing Subdural Signals in Humans for Communication and Diagnosis0 aUsing Subdural Signals in Humans for Communication and Diagnosis c11/10/20093 aEpilepsy Research Program, Georgetown University, Washington, DC.1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2009/using-subdural-signals-humans-communication-and-diagnosis01030nas a2200325 4500008004100000022001400041245010300055210006900158260001200227300001200239490000700251653002000258653002600278653002700304653002500331653002200356653002200378653002300400653001200423653002800435653002800463100002000491700001900511700002200530700002200552700002500574700002200599700001900621856006400640 2008 eng d a1529-240100aAdvanced neurotechnologies for chronic neural interfaces: new horizons and clinical opportunities.0 aAdvanced neurotechnologies for chronic neural interfaces new hor c11/2008 a11830-80 v2810aCerebral Cortex10aElectrodes, Implanted10aElectroencephalography10aElectronics, Medical10aElectrophysiology10aEvoked Potentials10aMovement Disorders10aNeurons10aProstheses and Implants10aUser-Computer Interface1 aKipke, Daryl, R1 aShain, William1 aBuzsáki, György1 aFetz, Eberhard, E1 aHenderson, Jaimie, M1 aHetke, Jamille, F1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/19005048?report=abstract00555nas a2200109 4500008004100000245007800041210006900119260001500188520010100203100001900304856012200323 2008 eng d00aBCI2000: A General-Purpose BCI System and its Application to ECoG Signals0 aBCI2000 A GeneralPurpose BCI System and its Application to ECoG  c11/15/20083 ag.tec Brain-Computer Interface Workshop, Society for Neuroscience Annual Meeting, Washington, DC1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/bci2000-general-purpose-bci-system-and-its-application-ecog-signals00469nas a2200109 4500008004100000245006300041210006000104260001500164520005100179100001900230856011000249 2008 eng d00aBCI2000: A General-Purpose Brain-Computer Interface System0 aBCI2000 A GeneralPurpose BrainComputer Interface System c07/05/20083 a4th BCI2000 Workshop, Utrecht, The Netherlands1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/bci2000-general-purpose-brain-computer-interface-system00356nas a2200109 4500008004100000245002600041210002200067260001500089520005100104100001900155856007200174 2008 eng d00aThe BCI2000 Framework0 aBCI2000 Framework c07/06/20083 a4th BCI2000 Workshop, Utrecht, The Netherlands1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/bci2000-framework00536nas a2200109 4500008004100000245004600041210004500087260001500132520016400147100001900311856009600330 2008 eng d00aBrain-Based Communication and Orientation0 aBrainBased Communication and Orientation c09/17/20083 aMulti-disciplinary University Research Initiative sponsored by the US Army Research Office. University of Maryland College Park, College Park, Maryland.1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/brain-based-communication-and-orientation05137nas a2200373 4500008004100000022001400041245007500055210006900130260001200199300001000211490000800221520406800229653001004297653001504307653001004322653001804332653002404350653002704374653001104401653000904412653002404421653002404445653001904469653003604488653004104524653002404565653002804589100001904617700001904636700002404655700001504679700002104694856004804715 2008 eng d a0165-027000aBrain-computer interfaces (BCIs): Detection Instead of Classification.0 aBraincomputer interfaces BCIs Detection Instead of Classificatio c01/2008 a51-620 v1673 a

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer through brain-computer interfaces (BCIs). These devices operate by recording signals from the brain and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into clinical applications is the current requirement for preliminary analyses to identify the brain signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting brain activity so that any change in relevant brain signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording brain signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.

10aAdult10aAlgorithms10aBrain10aBrain Mapping10aElectrocardiography10aElectroencephalography10aHumans10aMale10aMan-Machine Systems10aNormal Distribution10aOnline Systems10aSignal Detection, Psychological10aSignal Processing, Computer-Assisted10aSoftware Validation10aUser-Computer Interface1 aSchalk, Gerwin1 aBrunner, Peter1 aGerhardt, Lester, A1 aBischof, H1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1792013400574nas a2200109 4500008004100000245008300041210006900124260001500193520012500208100001900333856011200352 2008 eng d00aBrain-Computer Interfacing Using Electrocorticography and Electrocorticography0 aBrainComputer Interfacing Using Electrocorticography and Electro c01/18/20083 aEuroNeuro (European Congress on Neurology, Neurosurgery, Intensive Care and Anesthesiology), Maastricht, The Netherlands1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/brain-computer-interfacing-using-electrocorticography-and00502nas a2200109 4500008004100000245006500041210006200106260001500168520007500183100001900258856011500277 2008 eng d00aBrain-Computer Interfacing Using Electrocorticography (ECoG)0 aBrainComputer Interfacing Using Electrocorticography ECoG c01/24/20083 aMEG Center, Eberhard Karls University of Tübingen, Tübingen, Germany1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/brain-computer-interfacing-using-electrocorticography-ecog-100553nas a2200109 4500008004100000245006500041210006200106260001500168520012600183100001900309856011500328 2008 eng d00aBrain-Computer Interfacing Using Electrocorticography (ECoG)0 aBrainComputer Interfacing Using Electrocorticography ECoG c01/22/20083 aBernstein Seminar, Bernstein Center for Computation Neuroscience, Albert-Ludwigs-Universitaet Freiburg, Freiburg, Germany1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/brain-computer-interfacing-using-electrocorticography-ecog-000482nas a2200109 4500008004100000245006500041210006200106260001500168520005500183100001900238856011500257 2008 eng d00aBrain-Computer Interfacing Using Electrocorticography (ECoG)0 aBrainComputer Interfacing Using Electrocorticography ECoG c10/30/20083 aUniversity of Pittsburgh, Pittsburgh, Pennsylvania1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/brain-computer-interfacing-using-electrocorticography-ecog-200493nas a2200109 4500008004100000245006500041210006200106260001500168520006800183100001900251856011300270 2008 eng d00aBrain-Computer Interfacing Using Electrocorticography (ECoG)0 aBrainComputer Interfacing Using Electrocorticography ECoG c01/14/20083 aInstitute for Automation, University of Bremen, Bremen, Germany1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/brain-computer-interfacing-using-electrocorticography-ecog00520nas a2200109 4500008004100000245007100041210006900112260001500181520007400196100001900270856012100289 2008 eng d00aBrain-Computer Interfacing Using Non-Invasive and Invasive Methods0 aBrainComputer Interfacing Using NonInvasive and Invasive Methods c07/03/20083 aWorkshop Brain-Computer Interfacing in 2008, Utrecht, The Netherlands1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/brain-computer-interfacing-using-non-invasive-and-invasive-methods00541nas a2200109 4500008004100000245007100041210006900112260001500181520009300196100001900289856012300308 2008 eng d00aBrain-Computer Interfacing Using Non-Invasive and Invasive Methods0 aBrainComputer Interfacing Using NonInvasive and Invasive Methods c07/14/20083 aInstitute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/brain-computer-interfacing-using-non-invasive-and-invasive-methods-002262nas a2200193 4500008004100000022001400041245003000055210002800085260001200113300001100125490000600136520179600142653001001938653001401948653001101962653002801973100001902001856004802020 2008 eng d a1741-256000aBrain-computer symbiosis.0 aBraincomputer symbiosis c03/2008 aP1-P150 v53 a

The theoretical groundwork of the 1930s and 1940s and the technical advance of computers in the following decades provided the basis for dramatic increases in human efficiency. While computers continue to evolve, and we can still expect increasing benefits from their use, the interface between humans and computers has begun to present a serious impediment to full realization of the potential payoff. This paper is about the theoretical and practical possibility that direct communication between the brain and the computer can be used to overcome this impediment by improving or augmenting conventional forms of human communication. It is about the opportunity that the limitations of our body's input and output capacities can be overcome using direct interaction with the brain, and it discusses the assumptions, possible limitations and implications of a technology that I anticipate will be a major source of pervasive changes in the coming decades.

10aBrain10aComputers10aHumans10aUser-Computer Interface1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/1831080400645nas a2200109 4500008004100000245010300041210006900144260001500213520017600228100001900404856011200423 2008 eng d00aCombining Fidelity With Practicality: Interrogation of the Brain Using Electrocorticography (ECoG)0 aCombining Fidelity With Practicality Interrogation of the Brain  c11/17/20083 aSymposium Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities, Society for Neuroscience Annual Meeting, Washington, DC1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/combining-fidelity-practicality-interrogation-brain-using00673nas a2200109 4500008004100000245010200041210006900143260001500212520021200227100001900439856010500458 2008 eng d00aDecoding Detailed Information fromt he Brain Using Electrocorticographic (ECoG) Signals in Humans0 aDecoding Detailed Information fromt he Brain Using Electrocortic c10/24/20083 aLecture in Workshop on Research Efforts and Future Directions in Neuroergonamics and Neuromorphics sponsored by the US Army Research Office. University of Maryland Colelge Park, College Park, Maryland1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/decoding-detailed-information-fromt-he-brain-using00457nas a2200109 4500008004100000245005700041210005400098260001500152520006000167100001900227856010100246 2008 eng d00aA Device For Real-Time Functional Mapping Using ECoG0 aDevice For RealTime Functional Mapping Using ECoG c05/07/20083 aCIMIT Epilepsy Innovation Summit, Boston, Massachusetts1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/device-real-time-functional-mapping-using-ecog00509nas a2200109 4500008004100000245003800041210003800079260001500117520016700132100001900299856008100318 2008 eng d00aDirect Interaction With The Brain0 aDirect Interaction With The Brain c11/24/20083 aGuest lecture in Information Technology Capstone Course, Department of Information Technology, Rensselaer Polytechnic Institute, Troy, New York, 11/24/20081 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/direct-interaction-brain-000501nas a2200109 4500008004100000245003800041210003800079260001500117520016100132100001900293856007900312 2008 eng d00aDirect Interaction With The Brain0 aDirect Interaction With The Brain c10/08/20083 aGuest lecture in Business Issues for Engineers and Scientists, Department of Information Technology, Rensselaer Polytechnic Institute, Troy, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/direct-interaction-brain00563nas a2200145 4500008004100000245006700041210006500108260004100173100001900214700001700233700001900250700001300269700002200282856011300304 2008 eng d00aGeneral Clinical Issues Relevant to Brain-Computer Interfaces.0 aGeneral Clinical Issues Relevant to BrainComputer Interfaces aBoca RatonbTaylor and Francis Group1 aLeuthardt, E C1 aOjemann, J G1 aSchalk, Gerwin1 aMoran, D1 aDiLorenzo, Daniel uhttps://www.neurotechcenter.org/publications/2008/general-clinical-issues-relevant-brain-computer-interfaces00603nas a2200109 4500008004100000245007800041210006900119260001500188520015000203100001900353856012100372 2008 eng d00aInferring Details of Motor/Language Function Using ECoG Signals in Humans0 aInferring Details of MotorLanguage Function Using ECoG Signals i c12/09/20083 aWorkshop Advances in Theory and Clinical Application of Subdural Recordings, American Epilepsy Society Annual Meeting, Seattle, Washington1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/inferring-details-motorlanguage-function-using-ecog-signals-humans00588nas a2200109 4500008004100000245008100041210006900122260001500191520013600206100001900342856011700361 2008 eng d00aMovement Control Using Field Potentials Recorded on the Surface of the Brain0 aMovement Control Using Field Potentials Recorded on the Surface  c05/12/20083 aWorkshop on Real-Time Brain Interfacing Applications, Mathematical Biosciences Institute, The Ohio State University, Columbus, Ohio1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/movement-control-using-field-potentials-recorded-surface-brain04251nas a2200505 4500008004100000022001400041245009900055210006900154260001200223300001200235490000700247520283600254653003103090653001503121653001003136653001003146653001003156653002703166653002903193653001103222653001103233653001303244653000903257653001603266653001703282653003303299653001903332653002803351653001303379653002203392653001303414653004303427653002803470653001303498100001603511700002203527700001803549700002103567700001903588700002103607700002203628700002703650700002003677856004803697 2008 eng d a0361-923000aNon-invasive brain-computer interface system: towards its application as assistive technology.0 aNoninvasive braincomputer interface system towards its applicati c04/2008 a796-8030 v753 a

The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain-computer interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI.

10aActivities of Daily Living10aAdolescent10aAdult10aBrain10aChild10aElectroencephalography10aEvoked Potentials, Motor10aFemale10aHumans10aLearning10aMale10aMiddle Aged10aMotor Skills10aMuscular Dystrophy, Duchenne10aPilot Projects10aProstheses and Implants10aRobotics10aSelf-Help Devices10aSoftware10aSpinal Muscular Atrophies of Childhood10aUser-Computer Interface10aVolition1 aCincotti, F1 aMattia, Donatella1 aAloise, Fabio1 aBufalari, Simona1 aSchalk, Gerwin1 aOriolo, Giuseppe1 aCherubini, Andrea1 aMarciani, Maria Grazia1 aBabiloni, Fabio uhttp://www.ncbi.nlm.nih.gov/pubmed/1839452600418nas a2200109 4500008004100000245004500041210004500086260001500131520005100146100001900197856009200216 2008 eng d00aOverview of Available BCI2000 Components0 aOverview of Available BCI2000 Components c07/06/20083 a4th BCI2000 Workshop, Utrecht, The Netherlands1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/overview-available-bci2000-components03145nas a2200373 4500008004100000022001400041245005700055210005400112260001200166300001000178490000700188520212100195653001002316653001502326653001802341653002102359653003302380653002702413653001302440653002202453653001102475653001102486653000902497653003502506653003102541653003202572100001902604700001902623700001902642700001702661700002402678700002102702856004802723 2008 eng d a1095-957200aReal-time detection of event-related brain activity.0 aRealtime detection of eventrelated brain activity c11/2008 a245-90 v433 a

The complexity and inter-individual variation of brain signals impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex brain signals without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of brain function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize brain signal changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.

10aAdult10aAlgorithms10aBrain Mapping10aComputer Systems10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aEvoked Potentials10aFemale10aHumans10aMale10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity1 aSchalk, Gerwin1 aLeuthardt, E C1 aBrunner, Peter1 aOjemann, J G1 aGerhardt, Lester, A1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1871854400510nas a2200109 4500008004100000245006000041210006000101260001500161520010100176100001900277856010400296 2008 eng d00aTheory and Application of Subdural Recordings in Humans0 aTheory and Application of Subdural Recordings in Humans c12/18/20083 aNeurosciences Grand Rounds, The Neurosciences Institute, Albany Medical Center, Albany, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/theory-and-application-subdural-recordings-humans02177nas a2200361 4500008004100000022001400041245007200055210006900127260000900196300001200205520109300217653001501310653001001325653001501335653002401350653002901374653001101403653001101414653000901425653001601434653001701450653003501467653002401502653003401526653002801560100002001588700002101608700001901629700001701648700002101665700001701686856011201703 2008 eng d a1557-170X00aThree cases of feature correlation in an electrocorticographic BCI.0 aThree cases of feature correlation in an electrocorticographic B c2008 a5318-213 aThree human subjects participated in a closed-loop brain computer interface cursor control experiment mediated by implanted subdural electrocorticographic arrays. The paradigm consisted of several stages: baseline recording, hand and tongue motor tasks as the basis for feature selection, two closed-loop one-dimensional feedback experiments with each of these features, and a two-dimensional feedback experiment using both of the features simultaneously. The two selected features were simple channel and frequency band combinations associated with change during hand and tongue movement. Inter-feature correlation and cross-correlation between features during different epochs of each task were quantified for each stage of the experiment. Our anecdotal, three subject, result suggests that while high correlation between horizontal and vertical control signal can initially preclude successful two-dimensional cursor control, a feedback-based learning strategy can be successfully employed by the subject to overcome this limitation and progressively decorrelate these control signals.10aAdolescent10aAdult10aAlgorithms10aElectrocardiography10aEvoked Potentials, Motor10aFemale10aHumans10aMale10aMiddle Aged10aMotor Cortex10aPattern Recognition, Automated10aStatistics as Topic10aTask Performance and Analysis10aUser-Computer Interface1 aMiller, John, W1 aBlakely, Timothy1 aSchalk, Gerwin1 aNijs, Marcel1 aRao, Rajesh, P N1 aOjemann, J G uhttps://www.neurotechcenter.org/publications/2008/three-cases-feature-correlation-electrocorticographic-bci02352nas a2200445 4500008004100000245007200041210006900113260003300182520109300215653001501308653001001323653001501333653003401348653002001382653001801402653002401420653001501444653002701459653002801486653001301514653001101527653001401538653001401552653001101566653000901577653001601586653001701602653002201619653002401641653003401665653001101699653002801710100001901738700002101757700001901778700001701797700002001814700002401834856004801858 2008 eng d00aThree cases of feature correlation in an electrocorticographic BCI.0 aThree cases of feature correlation in an electrocorticographic B aVancouver, BCbIEEEc08/20083 aThree human subjects participated in a closed-loop brain computer interface cursor control experiment mediated by implanted subdural electrocorticographic arrays. The paradigm consisted of several stages: baseline recording, hand and tongue motor tasks as the basis for feature selection, two closed-loop one-dimensional feedback experiments with each of these features, and a two-dimensional feedback experiment using both of the features simultaneously. The two selected features were simple channel and frequency band combinations associated with change during hand and tongue movement. Inter-feature correlation and cross-correlation between features during different epochs of each task were quantified for each stage of the experiment. Our anecdotal, three subject, result suggests that while high correlation between horizontal and vertical control signal can initially preclude successful two-dimensional cursor control, a feedback-based learning strategy can be successfully employed by the subject to overcome this limitation and progressively decorrelate these control signals.10aAdolescent10aAdult10aAlgorithms10aautomated pattern recognition10acontrol systems10adecorrelation10aElectrocardiography10aElectrodes10aElectroencephalography10aevoked motor potentials10aFeedback10aFemale10afrequency10ahospitals10aHumans10aMale10aMiddle Aged10aMotor Cortex10aSignal Processing10aStatistics as Topic10aTask Performance and Analysis10aTongue10aUser-Computer Interface1 aMiller, Kai, J1 aBlakely, Timothy1 aSchalk, Gerwin1 aNijs, Marcel1 aRao, Rajesh, PN1 aOjemann, Jeffrey, G uhttp://www.ncbi.nlm.nih.gov/pubmed/1916391804357nas a2200385 4500008004100000022001400041245009700055210006900152260001200221300001200233490000800245520323100253653001503484653001003499653001403509653001003523653001803533653004203551653002703593653003003620653001103650653001103661653000903672653003203681653002303713653002203736653002803758100002503786700002603811700001903837700002003856700002603876700002103902856004803923 2008 eng d a1388-245700aTowards an independent brain-computer interface using steady state visual evoked potentials.0 aTowards an independent braincomputer interface using steady stat c02/2008 a399-4080 v1193 a

OBJECTIVE: 

Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze.

METHODS: 

Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs.

RESULTS: 

Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control.

CONCLUSIONS: 

The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user.

SIGNIFICANCE: 

SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.

10aAdolescent10aAdult10aAttention10aBrain10aBrain Mapping10aDose-Response Relationship, Radiation10aElectroencephalography10aEvoked Potentials, Visual10aFemale10aHumans10aMale10aPattern Recognition, Visual10aPhotic Stimulation10aSpectrum Analysis10aUser-Computer Interface1 aAllison, Brendan, Z.1 aMcFarland, Dennis, J.1 aSchalk, Gerwin1 aZheng, Shi Dong1 aMoore-Jackson, Melody1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1807720802662nas a2200409 4500008004100000022001400041245008400055210006900139260001200208300001000220490000600230520153900236653001501775653001001790653001801800653003701818653002001855653002401875653002601899653002701925653001301952653001101965653001101976653000901987653001301996653002802009100001902037700001602056700002602072700002002098700001602118700001702134700001302151700002102164700001902185856004802204 2008 eng d a1741-256000aTwo-dimensional movement control using electrocorticographic signals in humans.0 aTwodimensional movement control using electrocorticographic sign c03/2008 a75-840 v53 a

We show here that a brain-computer interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enables humans to control a computer cursor in two dimensions. Over a brief training period of 12-36 min, each of five human subjects acquired substantial control of particular ECoG features recorded from several locations over the same hemisphere, and achieved average success rates of 53-73% in a two-dimensional four-target center-out task in which chance accuracy was 25%. Our results support the expectation that ECoG-based BCIs can combine high performance with technical and clinical practicality, and also indicate promising directions for further research.

10aAdolescent10aAdult10aBrain Mapping10aData Interpretation, Statistical10aDrug Resistance10aElectrocardiography10aElectrodes, Implanted10aElectroencephalography10aEpilepsy10aFemale10aHumans10aMale10aMovement10aUser-Computer Interface1 aSchalk, Gerwin1 aMiller, K J1 aAnderson, Nicholas, R1 aWilson, Adam, J1 aSmyth, Matt1 aOjemann, J G1 aMoran, D1 aWolpaw, Jonathan1 aLeuthardt, E C uhttp://www.ncbi.nlm.nih.gov/pubmed/1831081304121nas a2200457 4500008004100000022001400041245010800055210006900163260001200232300001100244490000700255520290000262653001503162653001003177653002103187653001203208653001803220653001003238653002403248653002703272653001103299653000903310653001103319653000903330653001603339653001703355653001303372653001203385653002203397653002803419653001103447653002803458653001303486100002303499700002603522700001903548700001603567700001303583700001903596856004803615 2008 eng d a1524-462800aUnique cortical physiology associated with ipsilateral hand movements and neuroprosthetic implications.0 aUnique cortical physiology associated with ipsilateral hand move c12/2008 a3351-90 v393 a

BACKGROUND AND PURPOSE: 

Brain computer interfaces (BCIs) offer little direct benefit to patients with hemispheric stroke because current platforms rely on signals derived from the contralateral motor cortex (the same region injured by the stroke). For BCIs to assist hemiparetic patients, the implant must use unaffected cortex ipsilateral to the affected limb. This requires the identification of distinct electrophysiological features from the motor cortex associated with ipsilateral hand movements.

METHODS: 

In this study we studied 6 patients undergoing temporary placement of intracranial electrode arrays. Electrocorticographic (ECoG) signals were recorded while the subjects engaged in specific ipsilateral or contralateral hand motor tasks. Spectral changes were identified with regards to frequency, location, and timing.

RESULTS: 

Ipsilateral hand movements were associated with electrophysiological changes that occur in lower frequency spectra, at distinct anatomic locations, and earlier than changes associated with contralateral hand movements. In a subset of 3 patients, features specific to ipsilateral and contralateral hand movements were used to control a cursor on a screen in real time. In ipsilateral derived control this was optimal with lower frequency spectra.

CONCLUSIONS: 

There are distinctive cortical electrophysiological features associated with ipsilateral movements which can be used for device control. These findings have implications for patients with hemispheric stroke because they offer a potential methodology for which a single hemisphere can be used to enhance the function of a stroke induced hemiparesis.

10aAdolescent10aAdult10aArtificial Limbs10aBionics10aBrain Mapping10aChild10aDominance, Cerebral10aElectroencephalography10aFemale10aHand10aHumans10aMale10aMiddle Aged10aMotor Cortex10aMovement10aParesis10aProsthesis Design10aPsychomotor Performance10aStroke10aUser-Computer Interface10aVolition1 aWisneski, Kimberly1 aAnderson, Nicholas, R1 aSchalk, Gerwin1 aSmyth, Matt1 aMoran, D1 aLeuthardt, E C uhttp://www.ncbi.nlm.nih.gov/pubmed/1892745600516nas a2200109 4500008004100000245006500041210006500106260001500171520009000186100001900276856011100295 2008 eng d00aUsing Brain Signals for Clinical Diagnosis and Communication0 aUsing Brain Signals for Clinical Diagnosis and Communication c09/24/20083 aCenter for Neuropharmacology & Neuroscience, Albany Medical College, Albany, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/using-brain-signals-clinical-diagnosis-and-communication00556nas a2200109 4500008004100000245008900041210006900130260001500199520009100214100001900305856012200324 2008 eng d00aUsing Electrocorticographic (ECoG) Signals in Humans for Communication and Diagnosis0 aUsing Electrocorticographic ECoG Signals in Humans for Communica c12/10/20083 aDepartment of Physiology and Biophysics, University of Washington, Seattle, Washington1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/using-electrocorticographic-ecog-signals-humans-communication-and-000515nas a2200109 4500008004100000245008900041210006900130260001500199520005200214100001900266856012000285 2008 eng d00aUsing Electrocorticographic (ECoG) Signals in Humans for Communication and Diagnosis0 aUsing Electrocorticographic ECoG Signals in Humans for Communica c12/01/20083 aTechnical University of Berlin, Berlin, Germany1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/using-electrocorticographic-ecog-signals-humans-communication-and00617nas a2200109 4500008004100000245011500041210006900156260001500225520012700240100001900367856012100386 2008 eng d00aUsing Electrocorticography for Brain-Computer Interfacing and Detailed Single-Trial Decoding of Human Behavior0 aUsing Electrocorticography for BrainComputer Interfacing and Det c01/15/20083 aBrain Gain Lecture, F.C. Donders Center for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, The Netherlands1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2008/using-electrocorticography-brain-computer-interfacing-and-detailed00470nas a2200109 4500008004100000245006300041210006000104260001500164520005200179100001900231856011000250 2007 eng d00aBCI2000: A General-Purpose Brain-Computer Interface System0 aBCI2000 A GeneralPurpose BrainComputer Interface System c07/24/20073 a2nd BCI2000 Workshop, Beijing, China1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2007/bci2000-general-purpose-brain-computer-interface-system00447nas a2200109 4500008004100000245006700041210006400108260001400172100002300186700001900209856010900228 2007 eng d00aBCI2000: A General-Purpose Software Platform for BCI Research.0 aBCI2000 A GeneralPurpose Software Platform for BCI Research bMIT Press1 aMellinger, Jürgen1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2007/bci2000-general-purpose-software-platform-bci-research00522nas a2200109 4500008004100000245007200041210006900113260001500182520008200197100001900279856011400298 2007 eng d00aBrain-Computer Interfaces: Controlling A Computer With Your Thought0 aBrainComputer Interfaces Controlling A Computer With Your Though c10/25/20073 aScience Today Seminar Series. Bethlehem High School, Delmar, New York.1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2007/brain-computer-interfaces-controlling-computer-your-thought00507nas a2200109 4500008004100000245006500041210006200106260001500168520008200183100001900265856011300284 2007 eng d00aBrain-Computer Interfacing Using Electrocorticography (ECoG)0 aBrainComputer Interfacing Using Electrocorticography ECoG c07/23/20073 aInternational Workshop on Brain-Computer Interface Technology, Beijing, China1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2007/brain-computer-interfacing-using-electrocorticography-ecog00561nas a2200109 4500008004100000245007100041210006900112260001500181520011500196100001900311856012100330 2007 eng d00aBrain-Computer Interfacing Using Non-Invasive and Invasive Methods0 aBrainComputer Interfacing Using NonInvasive and Invasive Methods c12/04/20073 aComputer Science and Electrical Engineering Department, Orgegon Health & Science University, Beaverton, Oregon1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2007/brain-computer-interfacing-using-non-invasive-and-invasive-methods00525nas a2200109 4500008004100000245008800041210006900129260001500198520006400213100001900277856011900296 2007 eng d00aBrain-Computer Interfacing Using Non-Invasive, Intra-Cortical, and Subdural Methods0 aBrainComputer Interfacing Using NonInvasive IntraCortical and Su c09/25/20073 aMax-Planck-Institute for Brain Research, Frankfurt, Germany1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2007/brain-computer-interfacing-using-non-invasive-intra-cortical-and00530nas a2200109 4500008004100000245007600041210006900117260001500186520008600201100001900287856011400306 2007 eng d00aCommunicating Directly from the Brain: Brain-Computer Interfaces (BCIs)0 aCommunicating Directly from the Brain BrainComputer Interfaces B c06/19/20073 aWadsworth Center Research Experience for Undergraduates Program, Albany, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2007/communicating-directly-brain-brain-computer-interfaces-bcis02357nas a2200385 4500008004100000022001400041245009800055210006900153260001200222300001100234490000600245520131000251653001001561653001501571653000801586653001801594653002001612653002701632653002901659653001101688653001101699653000901710653001301719100001901732700001601751700002001767700002601787700001901813700001701832700001601849700001301865700002401878700002101902856004801923 2007 eng d a1741-256000aDecoding two-dimensional movement trajectories using electrocorticographic signals in humans.0 aDecoding twodimensional movement trajectories using electrocorti c09/2007 a264-750 v43 a

Signals from the brain could provide a non-muscular communication and control system, a brain-computer interface (BCI), for people who are severely paralyzed. A common BCI research strategy begins by decoding kinematic parameters from brain signals recorded during actual arm movement. It has been assumed that these parameters can be derived accurately only from signals recorded by intracortical microelectrodes, but the long-term stability of such electrodes is uncertain. The present study disproves this widespread assumption by showing in humans that kinematic parameters can also be decoded from signals recorded by subdural electrodes on the cortical surface (ECoG) with an accuracy comparable to that achieved in monkey studies using intracortical microelectrodes. A new ECoG feature labeled the local motor potential (LMP) provided the most information about movement. Furthermore, features displayed cosine tuning that has previously been described only for signals recorded within the brain. These results suggest that ECoG could be a more stable and less invasive alternative to intracortical electrodes for BCI systems, and could also prove useful in studies of motor function.

10aAdult10aAlgorithms10aArm10aBrain Mapping10aCerebral Cortex10aElectroencephalography10aEvoked Potentials, Motor10aFemale10aHumans10aMale10aMovement1 aSchalk, Gerwin1 aKubánek, J1 aMiller, John, W1 aAnderson, Nicholas, R1 aLeuthardt, E C1 aOjemann, J G1 aLimbrick, D1 aMoran, D1 aGerhardt, Lester, A1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1787342900598nas a2200109 4500008004100000245009200041210006900133260001500202520013400217100001900351856011800370 2007 eng d00aDefinition of Motor Responses and Brain-Computer Interfacing Using Electrocorticography0 aDefinition of Motor Responses and BrainComputer Interfacing Usin c07/06/20073 aNeuroscience Group, Laborator of Nervous System Disorders, Wadsworth Center, New York State Department of Health, Albany New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2007/definition-motor-responses-and-brain-computer-interfacing-using00488nas a2200109 4500008004100000245004000041210004000081260001500121520014200136100001900278856008100297 2007 eng d00aDirect Communication From the Brain0 aDirect Communication From the Brain c11/19/20073 aGuest lecture for BS in IT Capstone Course. Department of Information Technology, Rensselaer Polytechnic Institute, Troy, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2007/direct-communication-brain04391nas a2200409 4500008004100000022001400041245010700055210006900162260001200231300002900243490000700272520323000279653001003509653002203519653001803541653002503559653002603584653002703610653001103637653000903648653001103657653000903668653001603677653001703693653001703710653004103727653001103768100001903779700002003798700002603818700001903844700002003863700002003883700001303903700001703916856004803933 2007 eng d a1524-404000aElectrocorticographic Frequency Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex.0 aElectrocorticographic Frequency Alteration Mapping A Clinical Te c04/2007 a260-70; discussion 270-10 v603 a

OBJECTIVE: 

Electrocortical stimulation (ECS) has been well established for delineating the eloquent cortex. However, ECS is still coarse and inefficient in delineating regions of the functional cortex and can be hampered by after-discharges. Given these constraints, an adjunct approach to defining the motor cortex is the use of electrocorticographic signal changes associated with active regions of the cortex. The broad range of frequency oscillations are categorized into two main groups with respect to the sensorimotor cortex: low and high frequency bands. The low frequency bands tend to show a power reduction with cortical activation, whereas the high frequency bands show power increases. These power changes associated with the activated cortex could potentially provide a powerful tool in delineating areas of the motor cortex. We explore electrocorticographic signal alterations as they occur with activated regions of the motor cortex, as well as its potential in clinical brain mapping applications.

METHODS: 

We evaluated seven patients who underwent invasive monitoring for seizure localization. Each patient had extraoperative ECS mapping to identify the motor cortex. All patients also performed overt hand and tongue motor tasks to identify associated frequency power changes in regard to location and degree of concordance with ECS results that localized either hand or tongue motor function.

RESULTS: 

The low frequency bands had a high sensitivity (88.9-100%) and a lower specificity (79.0-82.6%) for identifying electrodes with either hand or tongue ECS motor responses. The high frequency bands had a lower sensitivity (72.7-88.9%) and a higher specificity (92.4-94.9%) in correlation with the same respective ECS positive electrodes.

CONCLUSION: 

The concordance between stimulation and spectral power changes demonstrate the possible utility of electrocorticographic frequency alteration mapping as an adjunct method to improve the efficiency and resolution of identifying the motor cortex.

10aAdult10aBiological Clocks10aBrain Mapping10aElectric Stimulation10aElectrodes, Implanted10aElectroencephalography10aFemale10aHand10aHumans10aMale10aMiddle Aged10aMotor Cortex10aOscillometry10aSignal Processing, Computer-Assisted10aTongue1 aLeuthardt, E C1 aMiller, John, W1 aAnderson, Nicholas, R1 aSchalk, Gerwin1 aDowling, Joshua1 aMiller, John, W1 aMoran, D1 aOjemann, J G uhttp://www.ncbi.nlm.nih.gov/pubmed/1741516200559nas a2200109 4500008004100000245007000041210006800111260001500179520012500194100001900319856011100338 2007 eng d00aElectrocorticography (ECoG) for Feedback and Decoding of Function0 aElectrocorticography ECoG for Feedback and Decoding of Function c12/08/20073 aWorkshop on Large Scale Brain Dynamics, Neural Information Processing Systems (NIPS), Whistler, British Columbia, Canada1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2007/electrocorticography-ecog-feedback-and-decoding-function00559nas a2200109 4500008004100000245008300041210006900124260001500193520010200208100001900310856012000329 2007 eng d00aElectrocorticography for Brain-Computer Interfacing and Motor/Language Mapping0 aElectrocorticography for BrainComputer Interfacing and MotorLang c02/22/20073 aNeurosciences Grand Rounds, The Neurosciences Institute, Albany Medical Center, Albany, New York.1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2007/electrocorticography-brain-computer-interfacing-and-motorlanguage03540nas a2200457 4500008004100000022001400041245004900055210004100104260001200145300001100157490000700168520234400175653001002519653001502529653001402544653001002558653002702568653002702595653002102622653001302643653001102656653000902667653000902676653001902685653001102704653003102715653002702746653000902773653001302782653003302795653004102828653002802869100002302897700001902920700002102939700002002960700001802980700002102998700001503019856004803034 2007 eng d a1053-811900aAn MEG-based brain-computer interface (BCI).0 aMEGbased braincomputer interface BCI c07/2007 a581-930 v363 a

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography(EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

10aAdult10aAlgorithms10aArtifacts10aBrain10aElectroencephalography10aElectromagnetic Fields10aElectromyography10aFeedback10aFemale10aFoot10aHand10aHead Movements10aHumans10aMagnetic Resonance Imaging10aMagnetoencephalography10aMale10aMovement10aPrincipal Component Analysis10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aMellinger, Jürgen1 aSchalk, Gerwin1 aBraun, Christoph1 aPreissl, Hubert1 aRosenstiel, W1 aBirbaumer, Niels1 aKübler, A uhttp://www.ncbi.nlm.nih.gov/pubmed/1747551101913nas a2200349 4500008004100000022001400041245007900055210006900134260001200203300001100215520081300226653001001039653003601049653002101085653001101106653003101117653002001148653002201168653001301190653002801203100001601231700001801247700002101265700001901286700002101305700002201326700002101348700002001369700002701389700002201416856012501438 2007 eng d a1557-170X00aNon-invasive brain-computer interface system to operate assistive devices.0 aNoninvasive braincomputer interface system to operate assistive  c04/2007 a2532-53 aIn this pilot study, a system that allows disabled persons to improve or recover their mobility and communication within the surrounding environment was implemented and validated. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Fourteen patients with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program. All users utilized regular assistive control options (e.g., microswitches or head trackers) while four patients learned to operate the system by means of a non-invasive EEG-based Brain-Computer Interface, based on the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp.10aBrain10aCommunication Aids for Disabled10aComputer Systems10aHumans10aNeurodegenerative Diseases10aQuality of Life10aSelf-Help Devices10aSoftware10aUser-Computer Interface1 aCincotti, F1 aAloise, Fabio1 aBufalari, Simona1 aSchalk, Gerwin1 aOriolo, Giuseppe1 aCherubini, Andrea1 aDavide, Fabrizio1 aBabiloni, Fabio1 aMarciani, Maria Grazia1 aMattia, Donatella uhttps://www.neurotechcenter.org/publications/2007/non-invasive-brain-computer-interface-system-operate-assistive-devices02238nas a2200325 4500008004100000022001400041245007500055210006900130260001200199300001200211490000700223520137400230653001001604653001801614653001101632653001101643653000901654653001601663653001701679653001301696100002001709700001901729700001901748700002101767700002601788700001301814700002001827700001701847856004801864 2007 eng d a1529-240100aSpectral Changes in Cortical Surface Potentials During Motor Movement.0 aSpectral Changes in Cortical Surface Potentials During Motor Mov c02/2007 a2424-320 v273 a

In the first large study of its kind, we quantified changes in electrocorticographic signals associated with motor movement across 22 subjects with subdural electrode arrays placed for identification of seizure foci. Patients underwent a 5-7 d monitoring period with array placement, before seizure focus resection, and during this time they participated in the study. An interval-based motor-repetition task produced consistent and quantifiable spectral shifts that were mapped on a Talairach-standardized template cortex. Maps were created independently for a high-frequency band (HFB) (76-100 Hz) and a low-frequency band (LFB) (8-32 Hz) for several different movement modalities in each subject. The power in relevant electrodes consistently decreased in the LFB with movement, whereas the power in the HFB consistently increased. In addition, the HFB changes were more focal than the LFB changes. Sites of power changes corresponded to stereotactic locations in sensorimotor cortex and to the results of individual clinical electrical cortical mapping. Sensorimotor representation was found to be somatotopic, localized in stereotactic space to rolandic cortex, and typically followed the classic homunculus with limited extrarolandic representation.

10aAdult10aBrain Mapping10aFemale10aHumans10aMale10aMiddle Aged10aMotor Cortex10aMovement1 aMiller, John, W1 aLeuthardt, E C1 aSchalk, Gerwin1 aRao, Rajesh, P N1 aAnderson, Nicholas, R1 aMoran, D1 aMiller, John, W1 aOjemann, J G uhttp://www.ncbi.nlm.nih.gov/pubmed/1732944102623nas a2200289 4500008004100000022001400041245008600055210007000141260001200211300001100223490000700234520175100241653001501992653002002007653002902027653002702056653002202083653001102105653001602116653003502132653002802167100002402195700001902219700002602238700002102264856004802285 2007 eng d a0018-929400aA µ-rhythm Matched Filter for Continuous Control of a Brain-Computer Interface.0 aµrhythm Matched Filter for Continuous Control of a BrainComputer c02/2007 a273-800 v543 a

A brain-computer interface (BCI) is a system that provides an alternate nonmuscular communication/control channel for individuals with severe neuromuscular disabilities. With proper training, individuals can learn to modulate the amplitude of specific electroencephalographic (EEG) components (e.g., the 8-12 Hz mu rhythm and 18-26 Hz beta rhythm) over the sensorimotor cortex and use them to control a cursor on a computer screen. Conventional spectral techniques for monitoring the continuousamplitude fluctuations fail to capture essential amplitude/phase relationships of the mu and beta rhythms in a compact fashion and, therefore, are suboptimal. By extracting the characteristic mu rhythm for a user, the exact morphology can be characterized and exploited as a matched filter. A simple, parameterized model for the characteristic mu rhythm is proposed and its effectiveness as a matched filter is examined online for a one-dimensional cursor control task. The results suggest that amplitude/phase coupling exists between the mu and beta bands during event-related desynchronization, and that an appropriate matched filter can provide improved performance.

10aAlgorithms10aCerebral Cortex10aCortical Synchronization10aElectroencephalography10aEvoked Potentials10aHumans10aImagination10aPattern Recognition, Automated10aUser-Computer Interface1 aKrusienski, Dean, J1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1727858403802nas a2200385 4500008004100000022001400041245008700055210006900142260001200211300001000223490000700233520264300240653001502883653001002898653003602908653002302944653002702967653002202994653001103016653002703027653002403054653003803078653002803116100002403144700002603168700002403194700001903218700002103237700002003258700002403278700002203302700002303324700002103347856004803368 2006 eng d a1534-432000aThe BCI competition III: Validating alternative approaches to actual BCI problems.0 aBCI competition III Validating alternative approaches to actual  c06/2006 a153-90 v143 a

brain-computer interface (BCI) is a system that allows its users to control external devices with brainactivity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.

10aAlgorithms10aBrain10aCommunication Aids for Disabled10aDatabases, Factual10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aSoftware Validation10aTechnology Assessment, Biomedical10aUser-Computer Interface1 aBlankertz, Benjamin1 aMüller, Klaus-Robert1 aKrusienski, Dean, J1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aSchlögl, Alois1 aPfurtscheller, Gert1 aMillán, José, R1 aSchröder, Michael1 aBirbaumer, Niels uhttp://www.ncbi.nlm.nih.gov/pubmed/1679228201675nas a2200397 4500008004100000022001400041245007000055210006600125260001200191300001100203490000700214520056900221653001500790653001800805653001000823653003600833653001400869653002700883653002100910653001100931653002100942653002400963653002700987653001301014653002801027100001601055700001501071700001601086700001301102700002301115700002201138700002301160700002701183700001901210856004801229 2006 eng d a1534-432000aBCI meeting 2005 - Workshop on Technology: Hardware and Software.0 aBCI meeting 2005 Workshop on Technology Hardware and Software c06/2006 a128-310 v143 a

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop to review and evaluate the current state of BCI-related hardware and software. Technical requirements and current technologies, standardization procedures and future trends are covered. The main conclusion was recognition of the need to focus technical requirements on the users' needs and the need for consistent standards in BCI research.

10aAlgorithms10aBiotechnology10aBrain10aCommunication Aids for Disabled10aComputers10aElectroencephalography10aEquipment Design10aHumans10aInternationality10aMan-Machine Systems10aNeuromuscular Diseases10aSoftware10aUser-Computer Interface1 aCincotti, F1 aBianchi, L1 aBirch, Gary1 aGuger, C1 aMellinger, Jürgen1 aScherer, Reinhold1 aSchmidt, Robert, N1 aYáñez Suárez, Oscar1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/1679227600545nas a2200109 4500008004100000245006000041210005800101260001500159520013700174100001900311856010500330 2006 eng d00aBCI2000: Software for Brain-Computer Interface Research0 aBCI2000 Software for BrainComputer Interface Research c11/30/20063 aNeuroscience Group, Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health, Albany, New York.1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2006/bci2000-software-brain-computer-interface-research00480nas a2200109 4500008004100000245003700041210003700078260001500115520013900130100001900269856008200288 2006 eng d00aBrain Interfacing with Materials0 aBrain Interfacing with Materials c11/10/20063 aTask group presentation, National Academy of Sciences Keck Futures Initiative "Smart Prosthetics." Beckman Center, Irvine, California.1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2006/brain-interfacing-materials00528nas a2200109 4500008004100000245006800041210006400109260001500173520009600188100001900284856011500303 2006 eng d00aBrain-Computer Interfaces (BCIs): Towards Clinical Applications0 aBrainComputer Interfaces BCIs Towards Clinical Applications c11/03/20063 aBiomedical Engineering Colloquium, Washington University in St. Louis, St. Louis, Missouri.1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2006/brain-computer-interfaces-bcis-towards-clinical-applications00506nas a2200109 4500008004100000245005900041210005700100260001500157520009700172100001900269856010800288 2006 eng d00aBrain-Computer Interfaces: Challenges and Perspectives0 aBrainComputer Interfaces Challenges and Perspectives c09/11/20063 aRudolf Magnus Institute of Neuroscience, University Medical Center, Utrecht, The Netherlands1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2006/brain-computer-interfaces-challenges-and-perspectives00444nas a2200109 4500008004100000245005500041210005200096260001500148520005300163100001900216856009900235 2006 eng d00aBrain-Computer Interfaces: Ready for Clinical Use?0 aBrainComputer Interfaces Ready for Clinical Use c03/02/20063 aCenter for Disability Services, Albany, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2006/brain-computer-interfaces-ready-clinical-use00445nas a2200109 4500008004100000245004500041210004400086260001500130520007600145100001900221856009500240 2006 eng d00aBrain-Computer Interfacing Using BCI20000 aBrainComputer Interfacing Using BCI2000 c09/20/20063 aKeynote address, g.tec Brain-Computer Interface Workshop, Graz, Austria1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2006/brain-computer-interfacing-using-bci200000517nas a2200109 4500008004100000245004000041210004000081260001500121520017100136100001900307856008100326 2006 eng d00aDirect Communication From the Brain0 aDirect Communication From the Brain c10/05/20063 aGuest lecture for course Services Science, Management, and Engineering. Department of Information Technology, Rensselaer Polytechnic Institute, Troy, New York.1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2006/direct-communication-brain02202nas a2200373 4500008004100000022001400041245007800055210006900133260001200202300001100214490000700225520114300232653001001375653001801385653002001403653003601423653002501459653002201484653001101506653001101517653001601528653000901544653002401553653002701577653002401604653002801628653001301656100002001669700002501689700002301714700001901737700002401756856004801780 2006 eng d a1534-432000aECoG factors underlying multimodal control of a brain-computer interface.0 aECoG factors underlying multimodal control of a braincomputer in c06/2006 a246-500 v143 a

Most current brain-computer interface (BCI) systems for humans use electroencephalographic activity recorded from the scalp, and may be limited in many ways. Electrocorticography (ECoG) is believed to be a minimally-invasive alternative to electroencephalogram (EEG) for BCI systems, yielding superior signal characteristics that could allow rapid user training and faster communication rates. In addition, our preliminary results suggest that brain regions other than the sensorimotor cortex, such as auditory cortex, may be trained to control a BCI system using similar methods as those used to train motor regions of the brain. This could prove to be vital for users who have neurological disease, head trauma, or other conditions precluding the use of sensorimotor cortex for BCI control.

10aAdult10aBrain Mapping10aCerebral Cortex10aCommunication Aids for Disabled10aComputer Peripherals10aEvoked Potentials10aFemale10aHumans10aImagination10aMale10aMan-Machine Systems10aNeuromuscular Diseases10aSystems Integration10aUser-Computer Interface10aVolition1 aWilson, Adam, J1 aFelton, Elizabeth, A1 aGarell, Charles, P1 aSchalk, Gerwin1 aWilliams, Justin, C uhttp://www.ncbi.nlm.nih.gov/pubmed/1679230501844nas a2200289 4500008004100000022001400041245008100055210006900136260001200205300001000217490000700227520100900234653002001243653002701263653001301290653002201303653001101325653003101336653002801367653001501395100001901410700002001429700001901449700002101468700001701489856004801506 2006 eng d a1534-432000aElectrocorticography-based brain computer interface--the Seattle experience.0 aElectrocorticographybased brain computer interfacethe Seattle ex c06/2006 a194-80 v143 a

Electrocorticography (ECoG) has been demonstrated to be an effective modality as a platform for brain-computer interfaces (BCIs). Through our experience with ten subjects, we further demonstrate evidence to support the power and flexibility of this signal for BCI usage. In a subset of four patients, closed-loop BCI experiments were attempted with the patient receiving online feedback that consisted of one-dimensional cursor movement controlled by ECoG features that had shown correlation with various real and imagined motor and speech tasks. All four achieved control, with final target accuracies between 73%-100%. We assess the methods for achieving control and the manner in which enhancing online control can be accomplished by rescreening during online tasks. Additionally, we assess the relevant issues of the current experimental paradigm in light of their clinical constraints.

10aCerebral Cortex10aElectroencephalography10aEpilepsy10aEvoked Potentials10aHumans10aTherapy, Computer-Assisted10aUser-Computer Interface10aWashington1 aLeuthardt, E C1 aMiller, John, W1 aSchalk, Gerwin1 aRao, Rajesh, P N1 aOjemann, J G uhttp://www.ncbi.nlm.nih.gov/pubmed/1679229203287nas a2200265 4500008004100000022001400041245007900055210006900134260001200203300002600215490000700241520252600248653001002774653001102784653002402795653001302819653001702832653002802849653002802877100001902905700001902924700001302943700001702956856004802973 2006 eng d a1524-404000aThe emerging world of motor neuroprosthetics: a neurosurgical perspective.0 aemerging world of motor neuroprosthetics a neurosurgical perspec c07/2006 a1-14; discussion 1-140 v593 a

A MOTOR NEUROPROSTHETIC device, or brain computer interface, is a machine that can take some type of signal from the brain and convert that information into overt device control such that it reflects the intentions of the user's brain. In essence, these constructs can decode the electrophysiological signals representing motor intent. With the parallel evolution of neuroscience, engineering, and rapid computing, the era of clinical neuroprosthetics is approaching as a practical reality for people with severe motor impairment. Patients with such diseases as spinal cord injury, stroke, limb loss, and neuromuscular disorders may benefit through the implantation of these brain computer interfaces that serve to augment their ability to communicate and interact with their environment. In the upcoming years, it will be important for the neurosurgeon to understand what a brain computer interface is, its fundamental principle of operation, and what the salient surgical issues are when considering implantation. We review the current state of the field of motor neuroprosthetics research, the early clinical applications, and the essential considerations from a neurosurgical perspective for the future.

10aBrain10aHumans10aMan-Machine Systems10aMovement10aNeurosurgery10aProstheses and Implants10aUser-Computer Interface1 aLeuthardt, E C1 aSchalk, Gerwin1 aMoran, D1 aOjemann, J G uhttp://www.ncbi.nlm.nih.gov/pubmed/1682329402977nas a2200361 4500008004100000022001400041245007400055210006800129260001200197300001100209490000700220520195300227653001202180653001002192653002702202653002202229653001102251653002702262653001302289653001302302653001602315653003102331653001702362653002802379100002402407700002602431700001902457700002502476700002402501700002102525700002102546856004802567 2006 eng d a1534-432000aThe Wadsworth BCI Research and Development Program: At Home with BCI.0 aWadsworth BCI Research and Development Program At Home with BCI c06/2006 a229-330 v143 a

The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the complexity of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.

10aAnimals10aBrain10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aNew York10aResearch10aSwitzerland10aTherapy, Computer-Assisted10aUniversities10aUser-Computer Interface1 aVaughan, Theresa, M1 aMcFarland, Dennis, J.1 aSchalk, Gerwin1 aSarnacki, William, A1 aKrusienski, Dean, J1 aSellers, Eric, W1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1679230100521nas a2200109 4500008004100000245006900041210006700110260001200177520009400189100001900283856010900302 2005 eng d00aCommunicating Directly from the Brain: Brain-Computer Interfaces0 aCommunicating Directly from the Brain BrainComputer Interfaces c11/20053 aCondensed Matter Physics Seminar Series. Rensselaer Polytechnic Institute, Troy, New York1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2005/communicating-directly-brain-brain-computer-interfaces02708nas a2200289 4500008004100000022001400041245010300055210006900158260001200227300001600239490000700255520184500262653002602107653001302133653001502146653002502161653001802186653001902204653002702223100001302250700002102263700002102284700001902305700002502324700002102349856004802370 2005 eng d a1529-240100aThe interaction of a new motor skill and an old one: H-reflex conditioning and locomotion in rats.0 ainteraction of a new motor skill and an old one Hreflex conditio c07/2005 a6898–69060 v253 aNew and old motor skills can interfere with each other or interact in other ways. Because each skill entails a distributed pattern of activity-dependent plasticity, investigation of their interactions is facilitated by simple models. In a well characterized model of simple learning, rats and monkeys gradually change the size of the H-reflex, the electrical analog of the spinal stretch reflex. This study evaluates in normal rats the interactions of this new skill of H-reflex conditioning with the old well established skill of overground locomotion. In rats in which the soleus H-reflex elicited in the conditioning protocol (i.e., the conditioning H-reflex) had been decreased by down-conditioning, the H-reflexes elicited during the stance and swing phases of locomotion (i.e., the locomotor H-reflexes) were also smaller. Similarly, in rats in which the conditioning H-reflex had been increased by up-conditioning, the locomotor H-reflexes were also larger. Soleus H-reflex conditioning did not affect the duration, length, or right/left symmetry of the step cycle. However, the conditioned change in the stance H-reflex was positively correlated with change in the amplitude of the soleus locomotor burst, and the correlation was consistent with current estimates of the contribution of primary afferent input to the burst. Although H-reflex conditioning and locomotion did not interfere with each other, H-reflex conditioning did affect how locomotion was produced: it changed soleus burst amplitude and may have induced compensatory changes in the activity of other muscles. These results illustrate and clarify the subtlety and complexity of skill interactions. They also suggest that H-reflex conditioning might be used to improve the abnormal locomotion produced by spinal cord injury or other disorders of supraspinal control.10aH-reflex conditioning10aLearning10aLocomotion10amemory consolidation10aMotor control10aRehabilitation10aspinal cord plasticity1 aChen, Yi1 aChen, Xiang Yang1 aJakeman, Lyn, B.1 aSchalk, Gerwin1 aStokes, Bradford, T.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1603389902315nas a2200241 4500008004100000022001400041245006000055210005800115260001200173300001400185490000800199520163200207653002101839653002501860653001701885653001601902100002301918700002301941700002101964700001901985700002102004856004802025 2005 eng d a0165-027000aLong-term spinal reflex studies in awake behaving mice.0 aLongterm spinal reflex studies in awake behaving mice c12/2005 a134–1430 v1493 aThe increasing availability of genetic variants of mice has facilitated studies of the roles of specific molecules in specific behaviors. The contributions of such studies could be strengthened and extended by correlation with detailed information on the patterns of motor commands throughout the course of specific behaviors in freely moving animals. Previously reported methodologies for long-term recording of electromyographic activity (EMG) in mice using implanted electrodes were designed for intermittent, but not continuous operation. This report describes the fabrication, implantation, and utilization of fine wire electrodes for continuous long-term recordings of spontaneous and nerve-evoked EMG in mice. Six mice were implanted with a tibial nerve cuff electrode and EMG electrodes in soleus and gastrocnemius muscles. Wires exited through a skin button and traveled through an armored cable to an electrical commutator. In mice implanted for 59-144 days, ongoing EMG was monitored continuously (i.e., 24 h/day, 7 days/week) by computer for 18-92 days (total intermittent recording for 25-130 days). When the ongoing EMG criteria were met, the computer applied the nerve stimulus, recorded the evoked EMG response, and determined the size of the M-response (MR) and the H-reflex (HR). It continually adjusted stimulation intensity to maintain a stable MR size. Stable recordings of ongoing EMG, MR, and HR were obtained typically 3 weeks after implantation. This study demonstrates the feasibility of long-term continuous EMG recordings in mice for addressing a variety of neurophysiological and behavioral issues.10aElectromyography10aimplanted electrodes10aMonosynaptic10aSpinal Cord1 aCarp, Jonathan, S.1 aTennissen, Ann, M.1 aChen, Xiang Yang1 aSchalk, Gerwin1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1602684800463nas a2200109 4500008004100000245006600041210006400107260001200171520005000183100001900233856010100252 2005 eng d00aNews from the Wadsworth BCI R&D Program: Pushing the Envelope0 aNews from the Wadsworth BCI RD Program Pushing the Envelope c04/20053 aEberhard-Karls University, Tübingen, Germany1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2005/news-wadsworth-bci-rd-program-pushing-envelope02332nas a2200457 4500008004100000022001400041245009000055210006900145260001200214300001100226490000700237520103300244653000901277653003401286653002701320653002901347653003701376653001101413653001101424653001601435653000901451653001601460653001701476653001301493653001401506653002301520653002801543653002501571653002201596653002801618100001501646700001501661700002301676700002401699700001601723700001901739700002601758700002101784700002101805856004801826 2005 eng d a1526-632X00aPatients with ALS can use sensorimotor rhythms to operate a brain-computer interface.0 aPatients with ALS can use sensorimotor rhythms to operate a brai c05/2005 a1775-70 v643 a

People with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS.

10aAged10aAmyotrophic Lateral Sclerosis10aElectroencephalography10aEvoked Potentials, Motor10aEvoked Potentials, Somatosensory10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aMotor Cortex10aMovement10aParalysis10aPhotic Stimulation10aProstheses and Implants10aSomatosensory Cortex10aTreatment Outcome10aUser-Computer Interface1 aKübler, A1 aNijboer, F1 aMellinger, Jürgen1 aVaughan, Theresa, M1 aPawelzik, H1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aBirbaumer, Niels1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1591180901195nas a2200253 4500008004100000022001400041245009000055210006900145260001200214300001600226490000700242520044000249653002800689100001500717700001600732700001700748700002400765700001700789700001900806700002600825700002100851700002100872856004800893 2005 eng d a1526-632X00aPatients with ALS can use sensorimotor rhythms to operate a brain-computer interface.0 aPatients with ALS can use sensorimotor rhythms to operate a brai c05/2005 a1775–17770 v643 aPeople with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS.10aUser-Computer Interface1 aKübler, A1 aNijboer, F.1 aMellinger, J1 aVaughan, Theresa, M1 aPawelzik, H.1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aBirbaumer, Niels1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1591180900403nas a2200109 4500008004100000245005200041210005100093260000900144100001900153700002100172856010000193 2005 eng d00aRecording Options for Brain-Computer Interfaces0 aRecording Options for BrainComputer Interfaces c20051 aSchalk, Gerwin1 aWolpaw, Jonathan uhttps://www.neurotechcenter.org/publications/2005/recording-options-brain-computer-interfaces-000404nas a2200109 4500008004100000245005300041210005100094260000900145100001900154700002100173856010000194 2005 eng d00aRecording Options for Brain-Computer Interfaces.0 aRecording Options for BrainComputer Interfaces c20051 aSchalk, Gerwin1 aWolpaw, Jonathan uhttps://www.neurotechcenter.org/publications/2005/recording-options-brain-computer-interfaces-100528nas a2200109 4500008004100000245005200041210005100093260001200144520014500156100001900301856009800320 2005 eng d00aRecording Options for Brain-Computer Interfaces0 aRecording Options for BrainComputer Interfaces c07/20053 aAugmented Cognition Conference / Satellite to 11th International Conference on Human-Computer Interaction, Caesars Palace, Las Vegas, Nevada1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2005/recording-options-brain-computer-interfaces00451nas a2200109 4500008004100000245004000041210004000081260001200121520009900133100001900232856009000251 2005 eng d00aTowards 2D Brain Control Using ECoG0 aTowards 2D Brain Control Using ECoG c07/20053 a11th International Conference on Human-Computer Interaction, Caesars Palace, Las Vegas, Nevada1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2005/towards-2d-brain-control-using-ecog00585nas a2200157 4500008004100000245008000041210006900121260000900190100001900199700001900218700001300237700001600250700001700266700002100283856012300304 2005 eng d00aTowards two-dimensional cursor control using electrocorticographic signals.0 aTowards twodimensional cursor control using electrocorticographi c20051 aSchalk, Gerwin1 aLeuthardt, E C1 aMoran, D1 aMiller, K J1 aOjemann, J G1 aWolpaw, Jonathan uhttps://www.neurotechcenter.org/publications/2005/towards-two-dimensional-cursor-control-using-electrocorticographic-000561nas a2200145 4500008004100000245007900041210006900120100001900189700001900208700001300227700001600240700001700256700002100273856012100294 2005 eng d00aTowards two-dimensional cursor control using electrocorticographic signals0 aTowards twodimensional cursor control using electrocorticographi1 aSchalk, Gerwin1 aLeuthardt, E C1 aMoran, D1 aMiller, K J1 aOjemann, J G1 aWolpaw, Jonathan uhttps://www.neurotechcenter.org/publications/2005/towards-two-dimensional-cursor-control-using-electrocorticographic00534nas a2200133 4500008004100000245007400041210006900115260001200184100002400196700001900220700002600239700002100265856011400286 2005 eng d00aTracking of the mu rhythm using an empirically derived matched filter0 aTracking of the mu rhythm using an empirically derived matched f c03/20051 aKrusienski, Dean, J1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttps://www.neurotechcenter.org/publications/2005/tracking-mu-rhythm-using-empirically-derived-matched-filter01351nas a2200409 4500008004100000020001800041245007500059210006900134260003300203653002700236653003000263653001900293653002900312653002500341653003300366653003400399653000800433653002700441653003900468653002100507653001700528653002000545653002900565653003000594653001500624653001800639653002300657653002600680653001100706653002300717653004000740100002400780700001900804700002600823700002100849856007100870 2005 eng d a0-7803-8710-400aTracking of the mu rhythm using an empirically derived matched filter.0 aTracking of the mu rhythm using an empirically derived matched f aArlington, VAbIEEEc03/200510abioelectric potentials10aBrain Computer Interfaces10abrain modeling10abrain-computer interface10acommunication device10acommunication system control10acortical mu rhythm modulation10aEEG10aElectroencephalography10aempirically derived matched filter10ahandicapped aids10alaboratories10amatched filters10amedical signal detection10amedical signal processing10amonitoring10amotor imagery10amu rhythm tracking10anoninvasive treatment10arhythm10asynchronous motors10atwo-dimensional cursor control data1 aKrusienski, Dean, J1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=141955902685nas a2200433 4500008004100000022001400041245011000055210006900165260001200234300001600246490000700262520140200269653003101671653000801702653001601710653002901726653000801755653000801763653002801771653003601799653001401835653000901849653001901858653003201877653002901909100002401938700002601962700001901988700002402007700001902031700002102050700002002071700002002091700002402111700002402135700002302159700002102182856004802203 2004 eng d a0018-929400aThe BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.0 aBCI Competition 2003 progress and perspectives in detection and  c06/2004 a1044–10510 v513 aInterest in developing a new method of man-to-machine communication–a brain-computer interface (BCI)–has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.10aaugmentative communication10aBCI10abeta-rhythm10abrain-computer interface10aEEG10aERP10aimagined hand movements10alateralized readiness potential10amu-rhythm10aP30010aRehabilitation10asingle-trial classification10aslow cortical potentials1 aBlankertz, Benjamin1 aMüller, Klaus-Robert1 aCurio, Gabriel1 aVaughan, Theresa, M1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aSchlögl, Alois1 aNeuper, Christa1 aPfurtscheller, Gert1 aHinterberger, Thilo1 aSchröder, Michael1 aBirbaumer, Niels uhttp://www.ncbi.nlm.nih.gov/pubmed/1518887602760nas a2200433 4500008004100000022001400041245011000055210006900165260001200234300001200246490000700258520140000265653001001665653001501675653003401690653002801724653001001752653001401762653002301776653002701799653002201826653001101848653003101859653003201890653002801922100002401950700002601974700001902000700002402019700001902043700002102062700002002083700002002103700002402123700002002147700002302167700002102190856011502211 2004 eng d a0018-929400aThe BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials.0 aBCI Competition 2003 Progress and perspectives in detection and  c06/2004 a1044-510 v513 aInterest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.10aAdult10aAlgorithms10aAmyotrophic Lateral Sclerosis10aArtificial Intelligence10aBrain10aCognition10aDatabases, Factual10aElectroencephalography10aEvoked Potentials10aHumans10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aBlankertz, Benjamin1 aMüller, Klaus-Robert1 aCurio, Gabriel1 aVaughan, Theresa, M1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aSchlögl, Alois1 aNeuper, Christa1 aPfurtscheller, Gert1 aHinterberger, T1 aSchröder, Michael1 aBirbaumer, Niels uhttps://www.neurotechcenter.org/publications/2004/bci-competition-2003-progress-and-perspectives-detection-and02279nas a2200205 4500008004100000022001400041245007000055210006400125260001200189300001600201490000700217520166200224653002801886100001901914700002601933700002401959700002101983700002102004856004802025 2004 eng d a0018-929400aBCI2000: a general-purpose brain-computer interface (BCI) system.0 aBCI2000 a generalpurpose braincomputer interface BCI system c06/2004 a1034–10430 v513 aMany laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.10aUser-Computer Interface1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aHinterberger, Thilo1 aBirbaumer, Niels1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1518887502705nas a2200337 4500008004100000022001400041245007000055210006400125260001200189300001200201490000700213520166200220653001501882653001001897653001401907653003601921653002501957653002701982653002102009653003102030653002202061653001102083653002402094653002802118100001902146700002602165700002002191700002102211700002102232856011402253 2004 eng d a0018-929400aBCI2000: a general-purpose brain-computer interface (BCI) system.0 aBCI2000 a generalpurpose braincomputer interface BCI system c06/2004 a1034-430 v513 aMany laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.10aAlgorithms10aBrain10aCognition10aCommunication Aids for Disabled10aComputer Peripherals10aElectroencephalography10aEquipment Design10aEquipment Failure Analysis10aEvoked Potentials10aHumans10aSystems Integration10aUser-Computer Interface1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aHinterberger, T1 aBirbaumer, Niels1 aWolpaw, Jonathan uhttps://www.neurotechcenter.org/publications/2004/bci2000-general-purpose-brain-computer-interface-bci-system03554nas a2200361 4500008004100000022001400041245007800055210006900133260001200202300001000214490000600224520253800230653001002768653001002778653003602788653002502824653003302849653002602882653002702908653002202935653001102957653001102968653001602979653000902995653002303004653002803027100001903055700001903074700002103093700001703114700001303131856004803144 2004 eng d a1741-256000aA brain-computer interface using electrocorticographic signals in humans.0 abraincomputer interface using electrocorticographic signals in h c06/2004 a63-710 v13 a

Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain.

10aAdult10aBrain10aCommunication Aids for Disabled10aComputer Peripherals10aDiagnosis, Computer-Assisted10aElectrodes, Implanted10aElectroencephalography10aEvoked Potentials10aFemale10aHumans10aImagination10aMale10aMovement Disorders10aUser-Computer Interface1 aLeuthardt, E C1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aOjemann, J G1 aMoran, D uhttp://www.ncbi.nlm.nih.gov/pubmed/1587662400456nas a2200109 4500008004100000245006300041210005900104260001200163520004100175100001900216856011100235 2004 eng d00aBrain-Computer Interfaces; EGI Amp Server; Event-Detection0 aBrainComputer Interfaces EGI Amp Server EventDetection c08/20043 aElectrical Geodesics, Eugene, Oregon1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2004/brain-computer-interfaces-egi-amp-server-event-detection00419nas a2200109 4500008004100000245005000041210004800091260001200139520004000151100001900191856009900210 2004 eng d00aBrain-Computer Interfaces: Present and Future0 aBrainComputer Interfaces Present and Future c06/20043 aFondazione Santa Lucia, Rome, Italy1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2004/brain-computer-interfaces-present-and-future00501nas a2200109 4500008004100000245005000041210004800091260001200139520012000151100001900271856010100290 2004 eng d00aBrain-Computer Interfaces: Present and Future0 aBrainComputer Interfaces Present and Future c10/20043 a"BrainDays" Symposium, Rudolf Magnus Institute of Neuroscience, University Medical Center, Utrecht, The Netherlands1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2004/brain-computer-interfaces-present-and-future-100431nas a2200109 4500008004100000245005000041210004800091260001200139520005000151100001900201856010100220 2004 eng d00aBrain-Computer Interfaces: Present and Future0 aBrainComputer Interfaces Present and Future c08/20043 aUniversity of Washington, Seattle, Washington1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2004/brain-computer-interfaces-present-and-future-000544nas a2200109 4500008004100000245006100041210005700102260001200159520013600171100001900307856010800326 2004 eng d00aBrain-Computer Interfaces: Signals, Methods, and Systems0 aBrainComputer Interfaces Signals Methods and Systems c02/20043 aSeminar SeriesNew Frontiers in Brain Machine Interfaces Research. Institute for Infocomm Research(I2R), Singapore1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2004/brain-computer-interfaces-signals-methods-and-systems00429nas a2200109 4500008004100000245002400041210002400065260001200089520012800101100001900229856007100248 2004 eng d00aBusiness in Austria0 aBusiness in Austria c05/20043 aInternational Business Panel, Executive MBA Program, Lally School of Management, Rensselaer Polytechnic Institute, Troy, NY1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2004/business-austria00416nas a2200109 4500008004100000245004600041210004500087260001200132520005000144100001900194856009300213 2004 eng d00aIntroduction to Brain-Computer Interfaces0 aIntroduction to BrainComputer Interfaces c06/20043 aUniversity of Rome "La Sapienza," Rome, Italy1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2004/introduction-brain-computer-interfaces00690nas a2200181 4500008004100000245009300041210006900134100002300203700001500226700001600241700001900257700002600276700002400302700002100326700002100347700001500368856012500383 2004 eng d00aP300 for communication: Evidence from patients with amyotrophic lateral sclerosis (ALS).0 aP300 for communication Evidence from patients with amyotrophic l1 aMellinger, Jürgen1 aNijboer, F1 aPawelzik, H1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aKuebler, A uhttps://www.neurotechcenter.org/publications/2004/p300-communication-evidence-patients-amyotrophic-lateral-sclerosis-als00504nas a2200109 4500008004100000245006100041210005700102260001200159520009400171100001900265856011000284 2003 eng d00aBrain-Computer Interfaces: Signals, Methods, and Systems0 aBrainComputer Interfaces Signals Methods and Systems c10/20033 aSociety for Neuroscience Hudson-Berkshire Chapter, State University of Albany, Albany, NY1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2003/brain-computer-interfaces-signals-methods-and-systems-100493nas a2200109 4500008004100000245006100041210005700102260001100159520008400170100001900254856011000273 2003 eng d00aBrain-Computer Interfaces: Signals, Methods, and Systems0 aBrainComputer Interfaces Signals Methods and Systems c8/20033 aWorld Congress on Medical Physics and Biomedical Engineering, Sydney, Australia1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2003/brain-computer-interfaces-signals-methods-and-systems-000474nas a2200109 4500008004100000245006100041210005700102260001200159520006400171100001900235856011000254 2003 eng d00aBrain-Computer Interfaces: Signals, Methods, and Systems0 aBrainComputer Interfaces Signals Methods and Systems c12/20033 aEberhard Karls University of Tübingen, Tübingen, Germany 1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2003/brain-computer-interfaces-signals-methods-and-systems-200457nas a2200109 4500008004100000245006100041210005700102260001200159520004900171100001900220856010800239 2003 eng d00aBrain-Computer Interfaces: Signals, Methods, and Systems0 aBrainComputer Interfaces Signals Methods and Systems c06/20033 aNASA Ames Research Center, Moffett Field, CA1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2003/brain-computer-interfaces-signals-methods-and-systems00429nas a2200109 4500008004100000245002400041210002400065260001200089520012800101100001900229856007100248 2003 eng d00aBusiness in Austria0 aBusiness in Austria c05/20033 aInternational Business Panel, Executive MBA Program, Lally School of Management, Rensselaer Polytechnic Institute, Troy, NY1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2003/business-austria03064nas a2200373 4500008004100000022001400041245009000055210006900145260001200214300001000226490000700236520200500243653002902248653001002277653001502287653001402302653001002316653001802326653002702344653003002371653001302401653001102414653001602425653002802441653001302469653002002482653002802502653002202530100002102552700002602573700002402599700001902623856004802642 2003 eng d a1534-432000aThe Wadsworth Center brain-computer interface (BCI) research and development program.0 aWadsworth Center braincomputer interface BCI research and develo c06/2003 a204-70 v113 a

Brain-computer interface (BCI) research at the Wadsworth Center has focused primarily on using electroencephalogram (EEG) rhythms recorded from the scalp over sensorimotor cortex to control cursor movement in one or two dimensions. Recent and current studies seek to improve the speed and accuracy of this control by improving the selection of signal features and their translation into device commands, by incorporating additional signal features, and by optimizing the adaptive interaction between the user and system. In addition, to facilitate the evaluation, comparison, and combination of alternative BCI methods, we have developed a general-purpose BCI system called BCI-2000 and have made it available to other research groups. Finally, in collaboration with several other groups, we are developing simple BCI applications and are testing their practicality and long-term value for people with severe motor disabilities.

10aAcademic Medical Centers10aAdult10aAlgorithms10aArtifacts10aBrain10aBrain Mapping10aElectroencephalography10aEvoked Potentials, Visual10aFeedback10aHumans10aMiddle Aged10aNervous System Diseases10aResearch10aResearch Design10aUser-Computer Interface10aVisual Perception1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/1289927500404nas a2200109 4500008004100000245004200041210004100083260001200124520004700136100001900183856009200202 2002 eng d00aBrain-Computer Interfaces and BCI20000 aBrainComputer Interfaces and BCI2000 c03/20023 aGeorgia State University, Atlanta, Georgia1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2002/brain-computer-interfaces-and-bci200000509nas a2200109 4500008004100000245006000041210005900101260001200160520010000172100001900272856010800291 2002 eng d00aBrain-Computer Interfaces for Communication and Control0 aBrainComputer Interfaces for Communication and Control c06/20023 a8th International Conference on Functional Mapping of the Human Brain, Sendai, Japan1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2002/brain-computer-interfaces-communication-and-control-000506nas a2200109 4500008004100000245005900041210005500100260001200155520010300167100001900270856010700289 2002 eng d00aGeneral-Purpose Brain-Computer Interfaces (BCI) System0 aGeneralPurpose BrainComputer Interfaces BCI System c10/20023 a33rd Neural Prosthesis Workshop, National Library of Medicine / NIH, Bethesda, Maryland1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2002/general-purpose-brain-computer-interfaces-bci-system02917nas a2200265 4500008004100000022001400041245009700055210006900152260001200221300001000233490000800243520213200251653002202383653001202405653002102417653001302438653001802451653002102469653000902490653004102499100001902540700002302559700002102582856004802603 2002 eng d a0165-027000aTemporal transformation of multiunit activity improves identification of single motor units.0 aTemporal transformation of multiunit activity improves identific c02/2002 a87-980 v1143 a

This report describes a temporally based method for identifying repetitive firing of motor units. This approach is ideally suited to spike trains with negative serially correlated inter-spike intervals (ISIs). It can also be applied to spike trains in which ISIs exhibit little serial correlation if their coefficient of variation (COV) is sufficiently low. Using a novel application of the Hough transform, this method (i.e. the modified Hough transform (MHT)) maps motor unit action potential (MUAP) firing times into a feature space with ISI and offset (defined as the latency from an arbitrary starting time to the first MUAP in the train) as dimensions. Each MUAP firing time corresponds to a pattern in the feature space that represents all possible MUAP trains with a firing at that time. Trains with stable ISIs produce clusters in the feature space, whereas randomly firing trains do not. The MHT provides a direct estimate of mean firing rate and its variability for the entire data segment, even if several individual MUAPs are obscured by firings from other motor units. Addition of this method to a shape-based classification approach markedly improved rejection of false positives using simulated data and identified spike trains in whole muscle electromyographic recordings from rats. The relative independence of the MHT from the need to correctly classify individual firings permits a global description of stable repetitive firing behavior that is complementary to shape-based approaches to MUAP classification.

10aAction Potentials10aAnimals10aElectromyography10aH-Reflex10aMotor Neurons10aMuscle, Skeletal10aRats10aSignal Processing, Computer-Assisted1 aSchalk, Gerwin1 aCarp, Jonathan, S.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1185004300490nas a2200109 4500008004100000245006000041210005900101260001200160520008300172100001900255856010600274 2001 eng d00aBrain-Computer Interfaces for Communication and Control0 aBrainComputer Interfaces for Communication and Control c12/20013 aNIPS*2001 Brain-Computer Interface Workshop, Whistler, British Columbia, Canda1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2001/brain-computer-interfaces-communication-and-control00493nas a2200121 4500008004100000245006000041210006000101260001200161520005400173100001900227700001900246856010600265 2001 eng d00aImproved Motor Unit Detection Using the Hough Transform0 aImproved Motor Unit Detection Using the Hough Transform c01/20013 aNeuro-Muscular Research Center, Boston University1 aSchalk, Gerwin1 aCarp, Jonathan uhttps://www.neurotechcenter.org/publications/2001/improved-motor-unit-detection-using-hough-transform00439nas a2200109 4500008004100000245004800041210004600089260001200135520006800147100001900215856009500234 2000 eng d00aBCI2000: A Generic Brain-Computer Interface0 aBCI2000 A Generic BrainComputer Interface c06/20003 aDepartment of Medical Informatics, Technical University of Graz1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2000/bci2000-generic-brain-computer-interface02866nas a2200289 4500008004100000022001400041245008600055210006900141260001200210300001400222490000600236520197100242653003102213653003502244653003302279100002102312700002102333700002202354700002602376700002002402700001902422700002102441700002102462700002102483700002402504856004802528 2000 eng d a1063-652800aBrain-computer interface technology: a review of the first international meeting.0 aBraincomputer interface technology a review of the first interna c06/2000 a164–1730 v83 aOver the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.10aaugmentative communication10aBrain-computer interface (BCI)10aelectroencephalography (EEG)1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aHeetderks, W., J.1 aMcFarland, Dennis, J.1 aPeckham, P., H.1 aSchalk, Gerwin1 aDonchin, Emanuel1 aQuatrano, L., A.1 aRobinson, C., J.1 aVaughan, Theresa, M uhttp://www.ncbi.nlm.nih.gov/pubmed/1089617803091nas a2200373 4500008004100000022001400041245008600055210006900141260001200210300001100222490000600233520197800239653001502217653002002232653003602252653002102288653002702309653002202336653001102358653002702369653004102396653002802437100002102465700002102486700001902507700002602526700001702552700001902569700002102588700001802609700001802627700002402645856004802669 2000 eng d a1063-652800aBrain-computer interface technology: a review of the first international meeting.0 aBraincomputer interface technology a review of the first interna c06/2000 a164-730 v83 a

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.

10aAlgorithms10aCerebral Cortex10aCommunication Aids for Disabled10aDisabled Persons10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aHeetderks, W J1 aMcFarland, Dennis, J.1 aPeckham, P H1 aSchalk, Gerwin1 aDonchin, Emanuel1 aQuatrano, L A1 aRobinson, C J1 aVaughan, Theresa, M uhttp://www.ncbi.nlm.nih.gov/pubmed/1089617801315nas a2200289 4500008004100000022001400041245006100055210005800116260001200174300001600186490000800202520045900210653003100669653002900700653002700729653002000756653002900776653002800805653001400833653001900847653002400866100001900890700002100909700002600930700002100956856004800977 2000 eng d a1388-245700aEEG-based communication: presence of an error potential.0 aEEGbased communication presence of an error potential c12/2000 a2138–21440 v1113 aEEG-based communication could be a valuable new augmentative communication technology for those with severe motor disabilities. Like all communication methods, it faces the problem of errors in transmission. In the Wadsworth EEG-based brain-computer interface (BCI) system, subjects learn to use mu or beta rhythm amplitude to move a cursor to targets on a computer screen. While cursor movement is highly accurate in trained subjects, it is not perfect.10aaugmentative communication10abrain-computer interface10aElectroencephalography10aerror potential10aerror related negativity10aevent related potential10amu rhythm10aRehabilitation10asensorimotor cortex1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aPfurtscheller, G uhttp://www.ncbi.nlm.nih.gov/pubmed/11090763