03178nas a2200421   4500008004100000022001400041245009800055210006900153260001200222300001100234490000700245520196800252653001002220653000902230653001502239653002402254653003002278653003602308653002702344653002102371653003102392653001102423653001102434653000902445653002402454653001602478653001702494653002202511653002802533100002502561700001602586700001902602700002002621700002202641700002102663700002402684856004802708       2014                        eng d  a1741-255200aA practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening.0 apractical intuitive braincomputer interface for communicating ye  c06/2014  a0350030 v113 aOBJECTIVE:
Previous work has shown that it is possible to build an EEG-based binary brain-computer interface system (BCI) driven purely by shifts of attention to auditory stimuli. However, previous studies used abrupt, abstract stimuli that are often perceived as harsh and unpleasant, and whose lack of inherent meaning may make the interface unintuitive and difficult for beginners. We aimed to establish whether we could transition to a system based on more natural, intuitive stimuli (spoken words 'yes' and 'no') without loss of performance, and whether the system could be used by people in the locked-in state.
APPROACH:
We performed a counterbalanced, interleaved within-subject comparison between an auditory streaming BCI that used beep stimuli, and one that used word stimuli. Fourteen healthy volunteers performed two sessions each, on separate days. We also collected preliminary data from two subjects with advanced amyotrophic lateral sclerosis (ALS), who used the word-based system to answer a set of simple yes-no questions.
MAIN RESULTS:
The N1, N2 and P3 event-related potentials elicited by words varied more between subjects than those elicited by beeps. However, the difference between responses to attended and unattended stimuli was more consistent with words than beeps. Healthy subjects' performance with word stimuli (mean 77% ± 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73% ± 2.8 s.e.). The two subjects with ALS used the word-based BCI to answer questions with a level of accuracy similar to that of the healthy subjects.
SIGNIFICANCE:
Since performance using word stimuli was at least as good as performance using beeps, we recommend that auditory streaming BCI systems be built with word stimuli to make the system more pleasant and intuitive. Our preliminary data show that word-based streaming BCI is a promising tool for communication by people who are locked in.10aAdult10aAged10aAlgorithms10aAuditory Perception10abrain-computer interfaces10aCommunication Aids for Disabled10aElectroencephalography10aEquipment Design10aEquipment Failure Analysis10aFemale10aHumans10aMale10aMan-Machine Systems10aMiddle Aged10aQuadriplegia10aTreatment Outcome10aUser-Computer Interface1 aHill, Jeremy, Jeremy1 aRicci, Erin1 aHaider, Sameah1 aMcCane, Lynn, M1 aHeckman, Susan, M1 aWolpaw, Jonathan1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/2483827802228nas a2200181   4500008004100000022001400041245004300055210004100098260001200139300001400151490000700165520173200172653002801904100002001932700002501952700002101977856004801998       2012                        eng d  a1942-554600aBrain-computer interfaces in medicine.0 aBraincomputer interfaces in medicine  c03/2012  a268–2790 v873 aBrain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function.10aUser-Computer Interface1 aShih, Jerry, J.1 aKrusienski, Dean, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2232536402062nas a2200241   4500008004100000022001400041245012200055210006900177260001200246300001100258490000600269520128800275653002801563100002001591700002601611700002401637700002101661700002401682700002301706700002201729700002101751856004801772       2012                        eng d  a1741-255200aEEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis.0 aEEG correlates of P300based braincomputer interface BCI performa  c04/2012  a0260140 v93 aThe purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5-8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for &gt;60% of the variance in copy-spelling performance (p &lt; 0.001, mean R(2)?= 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.10aUser-Computer Interface1 aMak, Joseph, N.1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aMcCane, Lynn, M.1 aTsui, Phillippa, Z.1 aZeitlin, Debra, J.1 aSellers, Eric, W.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2235050101835nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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?</span></p>10aAnimals10aBrain10aBrain Waves10aHumans10aMovement10aUser-Computer Interface1 aPei, Xiao-Mei1 aHill, Jeremy, Jeremy1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2234495102099nas a2200241   4500008004100000022001400041245011200055210006900167260001200236300001000248490000700258520136500265653001501630653001001645653002701655653001101682653001701693653002801710100002401738700002601762700002101788856004801809       2012                        eng d  a1873-274700aValue of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface.0 aValue of amplitude phase and coherence features for a sensorimot  c01/2012  a130-40 v873 aMeasures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, several off-line studies examined phase-locking value (PLV) as a possible feature for use in brain-computer interface (BCI) systems. However, only a few individuals have been studied and full statistical comparisons among the different classes of features and their combinations have not been conducted. The present study examines the relative BCI performance of spectral power, coherence, and PLV, alone and in combination. The results indicate that spectral power produced classification at least as good as PLV, coherence, or any possible combination of these measures. This may be due to the fact that all three measures reflect mainly the activity of a single signal source (i.e., an area of sensorimotor cortex). This possibility is supported by the finding that EEG signals from different channels generally had near-zero phase differences. Coherence, PLV, and other measures of inter-channel relationships may be more valuable for BCIs that use signals from more than one distinct cortical source.10aAlgorithms10aBrain10aElectroencephalography10aHumans10aMotor Cortex10aUser-Computer Interface1 aKrusienski, Dean, J1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2198598404066nas a2200277   4500008004100000022001400041245007100055210006900126260001200195300001100207490000700218520327200225653001003497653002003507653002703527653001103554653001103565653001603576653000903592653004103601653002803642100002703670700001803697700002503715856004803740       2011                        eng d  a1095-957200aCausal influence of gamma oscillations on the sensorimotor rhythm.0 aCausal influence of gamma oscillations on the sensorimotor rhyth  c05/2011  a837-420 v563 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Gamma oscillations of the electromagnetic field of the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;are known to be involved in a variety of cognitive processes, and are believed to be fundamental for information processing within the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. While gamma oscillations have been shown to be correlated with&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;rhythms at different frequencies, to date no empirical evidence has been presented that supports a causal influence of gamma oscillations on other&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;rhythms. In this work, we study the relation of gamma oscillations and the sensorimotor rhythm (SMR) in healthy human subjects using electroencephalography. We first demonstrate that modulation of the SMR, induced by motor imagery of either the left or right hand, is positively correlated with the power of frontal and occipital gamma oscillations, and negatively correlated with the power of centro-parietal gamma oscillations. We then demonstrate that the most simple causal structure, capable of explaining the observed correlation of gamma oscillations and the SMR, entails a causal influence of gamma oscillations on the SMR. This finding supports the fundamental role attributed to gamma oscillations for information processing within the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, and is of particular importance for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs). As modulation of the SMR is typically used in BCIs to infer a subject's intention, our findings entail that gamma oscillations have a causal influence on a subject's capability to utilize a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;for means of communication.</span></p>10aAdult10aCerebral Cortex10aElectroencephalography10aFemale10aHumans10aImagination10aMale10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aGrosse-Wentrup, Moritz1 aSchölkopf, B1 aHill, Jeremy, Jeremy  uhttp://www.ncbi.nlm.nih.gov/pubmed/2045162602734nas a2200349   4500008004100000022001400041245009000055210006900145260001200214300001100226490000600237520175300243653001001996653002902006653003702035653002802072653001102100653001102111653001602122653000902138653001302147653001302160653001002173653002802183100002302211700001402234700002502248700001802273700001802291700002702309856004802336       2011                        eng d  a1741-255200aClosing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery.0 aClosing the sensorimotor loop haptic feedback facilitates decodi  c06/2011  a0360050 v83 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The combination of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.</span></p>10aBrain10aEvoked Potentials, Motor10aEvoked Potentials, Somatosensory10aFeedback, Physiological10aFemale10aHumans10aImagination10aMale10aMovement10aRobotics10aTouch10aUser-Computer Interface1 aGomez-Rodriguez, M1 aPeters, J1 aHill, Jeremy, Jeremy1 aSchölkopf, B1 aGharabaghi, A1 aGrosse-Wentrup, Moritz  uhttp://www.ncbi.nlm.nih.gov/pubmed/2147487807041nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">A&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) provides a non-muscular communication channel to people with and without disabilities.&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;devices consist of hardware and software.&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;hardware records signals from the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, either invasively or non-invasively, using a series of device components.&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;software then translates these signals into device output commands and provides feedback. One may categorize different types of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;applications into the following four categories: basic&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">research</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, clinical/translational&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">research</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, consumer products, and emerging applications. These four categories use&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;hardware and software, but have different sets of requirements. For example, while basic&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">research</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;applications. The results indicate that&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;technology is in transition from isolated demonstrations to systematic&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">research</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and commercial&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">development</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. This process requires several multidisciplinary efforts, including the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">development</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;of better integrated and more robust&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;hardware and software, the definition of standardized&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, and the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">development</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">of certification, dissemination and reimbursement procedures.</span></p>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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</span></p>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/2175036902954nas a2200373   4500008004100000022001400041245008200055210006900137260001200206300001100218490000700229520184300236653002802079653002702107653002802134653002702162653002202189653003002211653001102241653001302252653002502265653002402290653001202314653004102326653002802367653001802395653002202413100001902435700001502454700002502469700002002494700001802514856004802532       2011                        eng d  a1530-888X00aA graphical model framework for decoding in the visual ERP-based BCI speller.0 agraphical model framework for decoding in the visual ERPbased BC  c01/2011  a160-820 v233 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.</span></p>10aArtificial Intelligence10aComputer User Training10aDiscrimination Learning10aElectroencephalography10aEvoked Potentials10aEvoked Potentials, Visual10aHumans10aLanguage10aModels, Neurological10aModels, Theoretical10aReading10aSignal Processing, Computer-Assisted10aUser-Computer Interface10aVisual Cortex10aVisual Perception1 aMartens, S M M1 aMooij, J M1 aHill, Jeremy, Jeremy1 aFarquhar, Jason1 aSchölkopf, B  uhttp://www.ncbi.nlm.nih.gov/pubmed/2096454002106nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</span></p>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/2203628700539nas a2200157   4500008004100000022001400041245010700055210006900162260001200231300001100243490000600254653002800260100002400288700002100312856004800333       2011                        eng d  a1741-255200aSpecial issue containing contributions from the Fourth International Brain-Computer Interface Meeting.0 aSpecial issue containing contributions from the Fourth Internati  c04/2011  a0202010 v810aUser-Computer Interface1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2143652200601nas 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/2118340404388nas a2200385   4500008004100000022001400041245009500055210006900150260001200219300001100231490000800242520325500250653001003505653003403515653002103549653001003570653003603580653002403616653002703640653002103667653001103688653000903699653004103708653002803749100002603777700002503803700001403828700001903842700001403861700001503875700002503890700002103915700001803936856004803954       2011                        eng d  a1872-895200aTransition from the locked in to the completely locked-in state: a physiological analysis.0 aTransition from the locked in to the completely lockedin state a  c06/2011  a925-330 v1223 a<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">OBJECTIVE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">To clarify the physiological and behavioral boundaries between locked-in (LIS) and the completely locked-in state (CLIS) (no voluntary eye movements, no communication possible) through electrophysiological data and to secure&nbsp;<span class="highlight">brain-computer-interface</span>&nbsp;(<span class="highlight">BCI</span>) communication.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electromyography from facial muscles, external anal sphincter (EAS), electrooculography and electrocorticographic data during different psychophysiological tests were acquired to define electrophysiological differences in an amyotrophic lateral sclerosis (ALS) patient with an intracranially implanted grid of 112 electrodes for nine months while the patient passed from the LIS to the CLIS.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">At the very end of the LIS there was no facial muscle activity, nor external anal sphincter but eye control. Eye movements were slow and lasted for short periods only. During CLIS event related&nbsp;<span class="highlight">brain</span>potentials (ERP) to passive limb movements and auditory stimuli were recorded, vibrotactile stimulation of different body parts resulted in no ERP response.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">CONCLUSIONS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The results presented contradict the commonly accepted assumption that the EAS is the last remaining muscle under voluntary control and demonstrate complete loss of eye movements in CLIS. The eye muscle was shown to be the last muscle group under voluntary control. The findings suggest ALS as a multisystem disorder, even affecting afferent sensory pathways.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">SIGNIFICANCE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Auditory and proprioceptive&nbsp;<span class="highlight">brain-computer-interface</span>&nbsp;(<span class="highlight">BCI</span>) systems are the only remaining communication channels in CLIS.</p>10aAdult10aAmyotrophic Lateral Sclerosis10aArea Under Curve10aBrain10aCommunication Aids for Disabled10aDisease Progression10aElectroencephalography10aElectromyography10aHumans10aMale10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aMurguialday, Ramos, A1 aHill, Jeremy, Jeremy1 aBensch, M1 aMartens, S M M1 aHalder, S1 aNijboer, F1 aSchoelkopf, Bernhard1 aBirbaumer, Niels1 aGharabaghi, A  uhttp://www.ncbi.nlm.nih.gov/pubmed/2088829203803nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electrocorticography (ECoG) has emerged as a new signal platform for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">BCI</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;operation and that these signals can be acquired from a cortical array that is small and minimally invasive.</span></p>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/2147163801801nas a2200181   4500008004100000022001400041245006700055210006200122260001200184300001400196490000700210520125900217653002801476100002201504700002401526700002101550856004801571       2010                        eng d  a1471-180X00aA brain-computer interface for long-term independent home use.0 abraincomputer interface for longterm independent home use  c10/2010  a449–4550 v113 aOur objective was to develop and validate a new brain-computer interface (BCI) system suitable for long-term independent home use by people with severe motor disabilities. The BCI was used by a 51-year-old male with ALS who could no longer use conventional assistive devices. Caregivers learned to place the electrode cap, add electrode gel, and turn on the BCI. After calibration, the system allowed the user to communicate via EEG. Re-calibration was performed remotely (via the internet), and BCI accuracy assessed in periodic tests. Reports of BCI usefulness by the user and the family were also recorded. Results showed that BCI accuracy remained at 83% (r = -.07, n.s.) for over 2.5 years (1.4% expected by chance). The BCI user and his family state that the BCI had restored his independence in social interactions and at work. He uses the BCI to run his NIH-funded research laboratory and to communicate via e-mail with family, friends, and colleagues. In addition to this first user, several other similarly disabled people are now using the BCI in their daily lives. In conclusion, long-term independent home use of this BCI system is practical for severely disabled people, and can contribute significantly to quality of life and productivity.10aUser-Computer Interface1 aSellers, Eric, W.1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2058394704192nas 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<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">OBJECTIVE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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).</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">INTERPRETATION:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</p>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/2051794302830nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</span></p>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/2085892401515nas a2200181   4500008004100000022001400041245007300055210006900128260001200197300001100209490000600220520095800226653002801184100002601212700002601238700002101264856004801285       2010                        eng d  a1741-255200aElectroencephalographic (EEG) control of three-dimensional movement.0 aElectroencephalographic EEG control of threedimensional movement  c06/2010  a0360070 v73 aBrain-computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses.10aUser-Computer Interface1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2046069001606nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</span></p>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/2040378101458nas a2200169   4500008004100000022001400041245011700055210006900172260001200241300001400253490000700267520089200274653002801166100002501194700002101219856004801240       2009                        eng d  a0074-774200aBrain-computer interface research at the wadsworth center developments in noninvasive communication and control.0 aBraincomputer interface research at the wadsworth center develop  c02/2009  a147–1570 v863 aBrain-computer interface (BCI) research at the Wadsworth Center focuses on noninvasive, electroencephalography (EEG)-based BCI methods for helping severely disabled individuals communicate and interact with their environment. We have demonstrated that these individuals, as well as able-bodied individuals, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one and two dimensions. We have also developed a practical P300-based BCI that enables users to access and control the full functionality of their personal computer. We are currently translating this laboratory-proved BCI technology into a system that can be used by severely disabled individuals in their homes with minimal ongoing technical oversight. Our comprehensive approach to BCI design has led to several innovations that are applicable in other BCI contexts, such as space missions.10aUser-Computer Interface1 aKrusienski, Dean, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1960799701983nas 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-temporal01326nas 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-bci200002156nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</span></p>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/1956989201338nas a2200301   4500008004100000022001400041245003800055210003500093260001300128300001100141490000700152520054900159653001400708653001000722653001800732653002500750653002500775653001100800653002100811653003500832653001700867653002800884100001900912700001700931700002500948700001500973856004800988       2009                        eng d  a1879-278200aA note on ethical aspects of BCI.0 anote on ethical aspects of BCI  c11/2009   a1352-70 v223 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">This paper focuses on ethical aspects of BCI, as a research and a clinical tool, that are challenging for practitioners currently working in the field. Specifically, the difficulties involved in acquiring informed consent from locked-in patients are investigated, in combination with an analysis of the shared moral responsibility in BCI teams, and the complications encountered in establishing effective communication with media.</span></p>10aBioethics10aBrain10aCommunication10aCommunications Media10aCooperative Behavior10aHumans10aInformed Consent10aProfessional-Patient Relations10aQuadriplegia10aUser-Computer Interface1 aHaselager, Pim1 aVlek, Rutger1 aHill, Jeremy, Jeremy1 aNijboer, F  uhttp://www.ncbi.nlm.nih.gov/pubmed/1961640501758nas a2200361   4500008004100000022001400041245012300055210006900178260001200247300001100259490000600270520064200276653001500918653001000933653001400943653002400957653002700981653003501008653001101043653002501054653003501079653002301114653001401137653004101151653003401192653002801226653001201254100001901266700002501285700002001310700001801330856004801348       2009                        eng d  a1741-255200aOverlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential.0 aOverlap and refractory effects in a braincomputer interface spel  c04/2009  a0260030 v63 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We reveal the presence of refractory and overlap effects in the event-related potentials in visual P300 speller datasets, and we show their negative impact on the performance of the system. This finding has important implications for how to encode the letters that can be selected for communication. However, we show that such effects are dependent on stimulus parameters: an alternative stimulus type based on apparent motion suffers less from the refractory effects and leads to an improved letter prediction performance.</span></p>10aAlgorithms10aBrain10aCognition10aComputer Simulation10aElectroencephalography10aEvent-Related Potentials, P30010aHumans10aModels, Neurological10aPattern Recognition, Automated10aPhotic Stimulation10aSemantics10aSignal Processing, Computer-Assisted10aTask Performance and Analysis10aUser-Computer Interface10aWriting1 aMartens, S M M1 aHill, Jeremy, Jeremy1 aFarquhar, Jason1 aSchölkopf, B  uhttp://www.ncbi.nlm.nih.gov/pubmed/1925546202198nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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].</span></p>10aBrain10aCalibration10aElectrodes10aElectroencephalography10aHumans10aUser-Computer Interface1 aWilson, Adam, J1 aSchalk, Gerwin1 aWalton, Léo M1 aWilliams, Justin, C  uhttp://www.ncbi.nlm.nih.gov/pubmed/1964147901030nas 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=abstract05137nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Many studies over the past two decades have shown that people can use&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals to convey their intent to a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;through&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs). These devices operate by recording signals from the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">clinical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;applications is the current requirement for preliminary analyses to identify the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;activity so that any change in&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">relevant</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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.</span></p>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/1792013401806nas a2200169   4500008004100000022001400041245006200055210006000117260001200177300001600189490000600205520130800211653002801519100002001547700002101567856004801588       2008                        eng d  a1474-442200aBrain-computer interfaces in neurological rehabilitation.0 aBraincomputer interfaces in neurological rehabilitation  c11/2008  a1032–10430 v73 aRecent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.10aUser-Computer Interface1 aDaly, Janis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1883554102262nas a2200193   4500008004100000022001400041245003000055210002800085260001200113300001100125490000600136520179600142653001001938653001401948653001101962653002801973100001902001856004802020       2008                        eng d  a1741-256000aBrain-computer symbiosis.0 aBraincomputer symbiosis  c03/2008  aP1-P150 v53 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, 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.</span></p>10aBrain10aComputers10aHumans10aUser-Computer Interface1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/1831080402228nas a2200289   4500008004100000022001400041245012100055210006900176260001200245300001100257490000900268520131200277653001501589653001401604653003301618653002701651653001301678653002901691653001701720653003101737653003201768653002801800100002001828700002301848700001901871856004801890       2008                        eng d  a1557-170X00aElectrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control.0 aElectrocorticographic interictal spike removal via denoising sou  c08/2008  a5224-70 v20083 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electrocorticographic (ECoG) neuroprosthesis is a promising area of research that could provide channels of communication and control for patients who have lost their motor functions due to damage to the nervous system. However, implantation of subdural electrodes are clinically restricted to diagnostics of pre-surgical epileptic patients. Hence, interictal activity is present in the recordings across various areas of the sensorimotor cortex and suppresses the amplitude modulated features extracted to model hand trajectories. Denoising source separation is a recently introduced framework which extracts hidden structures of interest within the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">data</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;through denoising the source estimates with filters designed around prior knowledge on the observations. Herein, we exploit the high amplitude quasiperiodic nature of the observed interictal spikes and show that removal of the interictal activity improves linear prediction of hand trajectories.</span></p>10aAlgorithms10aArtifacts10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aEvoked Potentials, Motor10aMotor Cortex10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aGunduz, Aysegul1 aSanchez, Justin, C1 aPrincipe, Jose  uhttp://www.ncbi.nlm.nih.gov/pubmed/1916389501945nas a2200193   4500008004100000022001400041245008500055210006900140260001200209300001400221490000600235520133600241653002801577100002601605700002501631700002601656700002101682856004801703       2008                        eng d  a1741-256000aEmulation of computer mouse control with a noninvasive brain-computer interface.0 aEmulation of computer mouse control with a noninvasive braincomp  c06/2008  a101–1100 v53 aBrain-computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the user's commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The user's task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection.10aUser-Computer Interface1 aMcFarland, Dennis, J.1 aKrusienski, Dean, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1836777905892nas a2200361   4500008004100000022001400041245010100055210006900156260001200225300001000237490000800247520480000255653001505055653002805070653001805098653002005116653002705136653002405163653001105187653000905198653001105207653003105218653003205249653002805281653004105309653002205350653002805372100002305400700002005423700002005443700001905463856004805482       2008                        eng d  a0165-027000aExtraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics.0 aExtraction and localization of mesoscopic motor control signals   c01/2008  a63-810 v1673 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Electrocorticogram (</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) recordings for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">neuroprosthetics</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;provide a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">mesoscopic</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;neural interfaces require appropriate feature&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">extraction</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and signal processing methods to identify and model in real-time signatures of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz)&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain-machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;voltages to determine if they provide viable&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;inputs for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;above the level of noisy background fluctuations in order to derive the commands for complex, continuous&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">motor</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;tasks using amplitude modulated&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">ECoG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">.</span></p>10aAdolescent10aBiofeedback, Psychology10aBrain Mapping10aCerebral Cortex10aElectroencephalography10aEpilepsies, Partial10aFemale10aHand10aHumans10aMagnetic Resonance Imaging10aPhysical Therapy Modalities10aPsychomotor Performance10aSignal Processing, Computer-Assisted10aSpectrum Analysis10aUser-Computer Interface1 aSanchez, Justin, C1 aGunduz, Aysegul1 aCarney, Paul, R1 aPrincipe, Jose  uhttp://www.ncbi.nlm.nih.gov/pubmed/1758250704251nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs) are systems that can translate&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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-</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">clinical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;demonstrations to a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">clinical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;useful BCI.</span></p>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/1839452601908nas a2200169   4500008004100000022001400041245012200055210006900177260001200246300001400258490000600272520133700278653002801615100002601643700002101669856004801690       2008                        eng d  a1741-256000aSensorimotor rhythm-based brain-computer interface (BCI): model order selection for autoregressive spectral analysis.0 aSensorimotor rhythmbased braincomputer interface BCI model order  c06/2008  a155–1620 v53 aPeople can learn to control EEG features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. Cursor movement depends on the estimate of the amplitudes of sensorimotor rhythms. Autoregressive models are often used to provide these estimates. The order of the autoregressive model has varied widely among studies. Through analyses of both simulated and actual EEG data, the present study examines the effects of model order on sensorimotor rhythm measurements and BCI performance. The results show that resolution of lower frequency signals requires higher model orders and that this requirement reflects the temporal span of the model coefficients. This is true for both simulated EEG data and actual EEG data during brain-computer interface (BCI) operation. Increasing model order, and decimating the signal were similarly effective in increasing spectral resolution. Furthermore, for BCI control of two-dimensional cursor movement, higher model orders produced better performance in each dimension and greater independence between horizontal and vertical movements. In sum, these results show that autoregressive model order selection is an important determinant of BCI performance and should be based on criteria that reflect system performance.10aUser-Computer Interface1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1843097401670nas a2200289   4500008004100000022001400041245011900055210006900174260001200243300001200255490000600267520076600273653004001039653002501079653002201104653002401126653001301150653002801163100002201191700001701213700001701230700002701247700002101274700001801295700001901313856004801332       2008                        eng d  a1750-279900aSPIDER image processing for single-particle reconstruction of biological macromolecules from electron micrographs.0 aSPIDER image processing for singleparticle reconstruction of bio  c10/2008  a1941-740 v33 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">This protocol describes the reconstruction of biological molecules from the electron micrographs of single particles. Computation here is performed using the image-processing software SPIDER and can be managed using a graphical user interface, termed the SPIDER Reconstruction Engine. Two approaches are described to obtain an initial reconstruction: random-conical tilt and common lines. Once an existing model is available, reference-based alignment can be used, a procedure that can be iterated. Also described is supervised classification, a method to look for homogeneous subsets when multiple known conformations of the molecule may coexist.</span></p>10aImage Processing, Computer-Assisted10aMicroscopy, Electron10aModels, Molecular10aMolecular Structure10aSoftware10aUser-Computer Interface1 aShaikh, Tanvir, R1 aGao, Haixiao1 aBaxter, Bill1 aAsturias, Francisco, J1 aBoisset, Nicolas1 aLeith, ArDean1 aFrank, Joachim  uhttp://www.ncbi.nlm.nih.gov/pubmed/1918007802177nas 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<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">OBJECTIVE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">CONCLUSIONS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The&nbsp;<span class="highlight">data</span>&nbsp;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.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">SIGNIFICANCE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</p>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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We show here that a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enables humans to control a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">clinical</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;practicality, and also indicate promising directions for further research.</span></p>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<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">BACKGROUND AND PURPOSE:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;"><span class="highlight">Brain computer interfaces</span>&nbsp;(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.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">METHODS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">RESULTS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</p>
<h4 style="font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;">CONCLUSIONS:&nbsp;</h4>
<p style="margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</p>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/1892745603153nas a2200481   4500008004100000022001400041245008200055210006900137260001200206300001000218490000700228520180600235653001002041653002802051653002002079653003602099653002402135653002702159653002402186653001102210653001102221653001602232653000902248653001602257653001902273653001702292653004102309653001302350653002502363653001702388653002802405653001202433100002002445700001802465700001302483700002502496700002402521700001802545700001802563700002102581700002102602856004802623       2008                        eng d  a1525-506900aVoluntary brain regulation and communication with electrocorticogram signals.0 aVoluntary brain regulation and communication with electrocortico  c08/2008  a300-60 v133 a<p><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.</span></p>10aAdult10aBiofeedback, Psychology10aCerebral Cortex10aCommunication Aids for Disabled10aDominance, Cerebral10aElectroencephalography10aEpilepsies, Partial10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aMotor Activity10aMotor Cortex10aSignal Processing, Computer-Assisted10aSoftware10aSomatosensory Cortex10aTheta Rhythm10aUser-Computer Interface10aWriting1 aHinterberger, T1 aWidman, Guido1 aLal, T N1 aHill, Jeremy, Jeremy1 aTangermann, Michael1 aRosenstiel, W1 aSchölkopf, B1 aElger, Christian1 aBirbaumer, Niels  uhttp://www.ncbi.nlm.nih.gov/pubmed/1849554102957nas a2200157   4500008004100000022001400041245006000055210005800115260001200173300001400185490000800199520249500207653002802702100002102730856004802751       2007                        eng d  a0022-375100aBrain-computer interfaces as new brain output pathways.0 aBraincomputer interfaces as new brain output pathways  c03/2007  a613–6190 v5793 aBrain-computer interfaces (BCIs) can provide non-muscular communication and control for people with severe motor disabilities. Current BCIs use a variety of invasive and non-invasive methods to record brain signals and a variety of signal processing methods. Whatever the recording and processing methods used, BCI performance (e.g. the ability of a BCI to control movement of a computer cursor) is highly variable and, by the standards applied to neuromuscular control, could be described as ataxic. In an effort to understand this imperfection, this paper discusses the relevance of two principles that underlie the brain's normal motor outputs. The first principle is that motor outputs are normally produced by the combined activity of many CNS areas, from the cortex to the spinal cord. Together, these areas produce appropriate control of the spinal motoneurons that activate muscles. The second principle is that the acquisition and life-long preservation of motor skills depends on continual adaptive plasticity throughout the CNS. This plasticity optimizes the control of spinal motoneurons. In the light of these two principles, a BCI may be viewed as a system that changes the outcome of CNS activity from control of spinal motoneurons to, instead, control of the cortical (or other) area whose signals are used by the BCI to determine the user's intent. In essence, a BCI attempts to assign to cortical neurons the role normally performed by spinal motoneurons. Thus, a BCI requires that the many CNS areas involved in producing normal motor actions change their roles so as to optimize the control of cortical neurons rather than spinal motoneurons. The disconcerting variability of BCI performance may stem in large part from the challenge presented by the need for this unnatural adaptation. This difficulty might be reduced, and BCI development might thereby benefit, by adopting a 'goal-selection' rather than a 'process- control' strategy. In 'process control', a BCI manages all the intricate high-speed interactions involved in movement. In 'goal selection', by contrast, the BCI simply communicates the user's goal to software that handles the high-speed interactions needed to achieve the goal. Not only is 'goal selection' less demanding, but also, by delegating lower-level aspects of motor control to another structure (rather than requiring that the cortex do everything), it more closely resembles the distributed operation characteristic of normal motor control.10aUser-Computer Interface1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1725516403540nas a2200457   4500008004100000022001400041245004900055210004100104260001200145300001100157490000700168520234400175653001002519653001502529653001402544653001002558653002702568653002702595653002102622653001302643653001102656653000902667653000902676653001902685653001102704653003102715653002702746653000902773653001302782653003302795653004102828653002802869100002302897700001902920700002102939700002002960700001802980700002102998700001503019856004803034       2007                        eng d  a1053-811900aAn MEG-based brain-computer interface (BCI).0 aMEGbased braincomputer interface BCI  c07/2007  a581-930 v363 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">electroencephalography</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">(</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) 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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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.</span></p>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-devices02623nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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 (</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) 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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">continuous</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">amplitude 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.</span></p>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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">A&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCI) is a system that allows its users to control external devices with&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">activity. 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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, which produces&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">online</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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.</span></p>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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</span></p>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/1679227602785nas a2200445   4500008004100000022001400041245015300055210006900208260001200277300001000289490000700299520147600306653001501782653002801797653002101825653002701846653002701873653002201900653001101922653001101933653001601944653000901960653001601969653001401985653003501999653002802034100002502062700001302087700002302100700002002123700002102143700001502164700001902179700001802198700002102216700001802237700001502255700002102270856004802291       2006                        eng d  a1534-432000aClassifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects.0 aClassifying EEG and ECoG signals without subject training for fa  c06/2006  a183-60 v143 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">We summarize results from a series of related studies that aim to develop a motor-imagery-</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">based</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain-computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.</span></p>10aAlgorithms10aArtificial Intelligence10aCluster Analysis10aComputer User Training10aElectroencephalography10aEvoked Potentials10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aParalysis10aPattern Recognition, Automated10aUser-Computer Interface1 aHill, Jeremy, Jeremy1 aLal, T N1 aSchröder, Michael1 aHinterberger, T1 aWilhelm, Barbara1 aNijboer, F1 aMochty, Ursula1 aWidman, Guido1 aElger, Christian1 aSchölkopf, B1 aKübler, A1 aBirbaumer, Niels  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679228902202nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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 (</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">) 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.</span></p>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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</span></p>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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">A MOTOR NEUROPROSTHETIC device, or&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, is a machine that can take some type of signal from the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;and convert that information into overt device control such that it reflects the intentions of the user's&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. 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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain computer interface</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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.</span></p>10aBrain10aHumans10aMan-Machine Systems10aMovement10aNeurosurgery10aProstheses and Implants10aUser-Computer Interface1 aLeuthardt, E C1 aSchalk, Gerwin1 aMoran, D1 aOjemann, J G  uhttp://www.ncbi.nlm.nih.gov/pubmed/1682329401404nas a2200181   4500008004100000022001400041245011100055210006900166260001200235300001600247490000600263520080700269653002801076100002301104700002601127700002101153856004801174       2006                        eng d  a1557-170X00aAn evaluation of autoregressive spectral estimation model order for brain-computer interface applications.0 aevaluation of autoregressive spectral estimation model order for  c09/2006  a1323–13260 v13 aAutoregressive (AR) spectral estimation is a popular method for modeling the electroencephalogram (EEG), and therefore the frequency domain EEG phenomena that are used for control of a brain-computer interface (BCI). Several studies have been conducted to evaluate the optimal AR model order for EEG, but the criteria used in these studies does not necessarily equate to the optimal AR model order for sensorimotor rhythm (SMR)-based BCI control applications. The present study confirms this by evaluating the EEG spectra of data obtained during control of SMR-BCI using different AR model orders and model evaluation criteria. The results indicate that the AR model order that optimizes SMR-BCI control performance is generally higher than the model orders that are frequently used in SMR-BCI studies.10aUser-Computer Interface1 aKrusienski, D., J.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1794603804762nas a2200325   4500008004100000022001400041245009800055210006900153260001200222300000900234490000600243520379900249653001504048653002704063653002904090653001104119653001604130653001704146653001304163653003504176653003104211653003204242653002804274100001804302700002004320700001204340700001804352700001804370856004804388       2006                        eng d  a1741-256000aMulti-channel linear descriptors for event-related EEG collected in brain computer interface.0 aMultichannel linear descriptors for eventrelated EEG collected i  c03/2006  a52-80 v33 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">By three multi-channel linear descriptors, i.e. spatial&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">complexity</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(omega), field power (sigma) and frequency of field changes (phi),&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">event-related</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">data</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">event-related</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">data</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">event-related</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">event-related</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;patterns in the application of brain computer interfaces.</span></p>10aAlgorithms10aElectroencephalography10aEvoked Potentials, Motor10aHumans10aImagination10aMotor Cortex10aMovement10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aPei, Xiao-Mei1 aZheng, Shi Dong1 aXu, Jin1 aBin, Guang-yu1 aWang, Zuoguan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1651094201922nas a2200169   4500008004100000022001400041245009700055210006900152260001200221300001400233490000700247520137700254653002801631100002401659700002101683856004801704       2006                        eng d  a1534-432000aThe Third International Meeting on Brain-Computer Interface Technology: making a difference.0 aThird International Meeting on BrainComputer Interface Technolog  c06/2006  a126–1270 v143 aThis special issue of the IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING provides a representative and comprehensive bird's-eye view of the most recent developments in brain-computer interface (BCI) technology from laboratories around the world. The 30 research communications and papers are the direct outcome of the Third International Meeting on Brain-Computer Interface Technology held at the Rensselaerville Institute, Rensselaerville, NY, in June 2005. Fifty-three research groups from North and South America, Europe, and Asia, representing the majority of all the existing BCI laboratories around the world, participated in this highly focused meeting sponsored by the National Institutes of Health and organized by the BCI Laboratory of the Wadsworth Center of the New York State Department of Health. As demonstrated by the papers in this special issue, the rapid advances in BCI research and development make this technology capable of providing communication and control to people severely disabled by amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy, and other neuromuscular disorders. Future work is expected to improve the performance and utility of BCIs, and to focus increasingly on making them a viable, practical, and affordable communication alternative for many thousands of severely disabled people worldwide.10aUser-Computer Interface1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679227502977nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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,&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">electroencephalography</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">EEG</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">)-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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">complexity</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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.</span></p>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/1679230101195nas 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/1591180902332nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">People with severe motor disabilities can maintain an acceptable quality of life if they can communicate.&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(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-</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">based</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;BCI could help maintain quality of life for people with ALS.</span></p>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/1591180902760nas 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<p><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain-computer interfaces</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;(BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. 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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;can enable users to control a one-dimensional&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">computer</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">brain</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">.</span></p>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/1587662403064nas 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<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;cursor movement in one or two dimensions. Recent and current studies seek to improve the speed and accuracy of this&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;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&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">groups</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">. Finally, in collaboration with several other&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">groups</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, we are developing simple BCI applications and are testing their practicality and long-term value for people with severe motor disabilities.</span></p>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/1289927506596nas a2200265   4500008004100000022001400041245006100055210005900116260001200175300001100187490000800198520581800206653001906024653003606043653002106079653002706100653001106127653002806138100002106166700002106187700002606208700002406234700002406258856004806282       2002                        eng d  a1388-245700aBrain-computer interfaces for communication and control.0 aBraincomputer interfaces for communication and control  c06/2002  a767-910 v1133 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world - a brain-computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or 'locked in', with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10-25bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;option for those with motor disabilities and might also give those without disabilities a supplementary&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;channel or a&nbsp;</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">control</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">&nbsp;channel useful in special circumstances.</span></p>10aBrain Diseases10aCommunication Aids for Disabled10aComputer Systems10aElectroencephalography10aHumans10aUser-Computer Interface1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aMcFarland, Dennis, J.1 aPfurtscheller, Gert1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/1204803801388nas a2200205   4500008004100000022001400041245005900055210005600114260001200170300001400182490000800196520081100204653002801015100001501043700001901058700001501077700002101092700002101113856004801134       2001                        eng d  a0033-290900aBrain-computer communication: unlocking the locked in.0 aBraincomputer communication unlocking the locked in  c05/2001  a358–3750 v1273 aWith the increasing efficiency of life-support systems and better intensive care, more patients survive severe injuries of the brain and spinal cord. Many of these patients experience locked-in syndrome: The active mind is locked in a paralyzed body. Consequently, communication is extremely restricted or impossible. A muscle-independent communication channel overcomes this problem and is realized through a brain-computer interface, a direct connection between brain and computer. The number of technically elaborated brain-computer interfaces is in contrast with the number of systems used in the daily life of locked-in patients. It is hypothesized that a profound knowledge and consideration of psychological principles are necessary to make brain-computer interfaces feasible for locked-in patients.10aUser-Computer Interface1 aKübler, A1 aKotchoubey, B.1 aKaiser, J.1 aWolpaw, Jonathan1 aBirbaumer, Niels  uhttp://www.ncbi.nlm.nih.gov/pubmed/1139330101157nas a2200181   4500008004100000022001400041245006300055210006100118260001200179300001400191490000600205520061700211653002800828100002100856700002600877700002400903856004800927       2000                        eng d  a1063-652800aBrain-computer interface research at the Wadsworth Center.0 aBraincomputer interface research at the Wadsworth Center  c06/2000  a222–2260 v83 aStudies at the Wadsworth Center over the past 14 years have shown that people with or without motor disabilities can learn to control the amplitude of mu or beta rhythms in electroencephalographic (EEG) activity recorded from the scalp over sensorimotor cortex and can use that control to move a cursor on a computer screen in one or two dimensions. This EEG-based brain-computer interface (BCI) could provide a new augmentative communication technology for those who are totally paralyzed or have other severe motor impairments. Present research focuses on improving the speed and accuracy of BCI communication.10aUser-Computer Interface1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/1089619403091nas 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<p>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.</p>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/1089617802346nas a2200181   4500008004100000022001400041245008500055210006900140260001200209300001600221490000700237520178000244653002802024100001802052700002602070700002102096856004702117       1998                        eng d  a0003-999300aAnswering questions with an electroencephalogram-based brain-computer interface.0 aAnswering questions with an electroencephalogrambased braincompu  c09/1998  a1029–10330 v793 aOBJECTIVE:
To demonstrate that humans can learn to control selected electroencephalographic components and use that control to answer simple questions.
METHODS:
Four adults (one with amyotrophic lateral sclerosis) learned to use electroencephalogram (EEG) mu rhythm (8 to 12Hz) or beta rhythm (18 to 25Hz) activity over sensorimotor cortex to control vertical cursor movement to targets at the top or bottom edge of a video screen. In subsequent sessions, the targets were replaced with the words YES and NO, and individuals used the cursor to answer spoken YES/NO questions from single- or multiple-topic question sets. They confirmed their answers through the response verification (RV) procedure, in which the word positions were switched and the question was answered again.
RESULTS:
For 5 consecutive sessions after initial question training, individuals were asked an average of 4.0 to 4.6 questions per minute; 64% to 87% of their answers were confirmed by the RV procedure and 93% to 99% of these answers were correct. Performances for single- and multiple-topic question sets did not differ significantly.
CONCLUSIONS:
The results indicate that (1) EEG-based cursor control can be used to answer simple questions with a high degree of accuracy, (2) attention to auditory queries and formulation of answers does not interfere with EEG-based cursor control, (3) question complexity (at least as represented by single versus multiple-topic question sets) does not noticeably affect performance, and (4) the RV procedure improves accuracy as expected. Several options for increasing the speed of communication appear promising. An EEG-based brain-computer interface could provide a new communication and control modality for people with severe motor disabilities.10aUser-Computer Interface1 aMiner, L., A.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/974967801530nas a2200205   4500008004100000022001400041245011100055210006900166260000900235300001400244490000700258520087900265653002801144100002101172700001901193700001801212700002101230700002601251856004701277       1995                        eng d  a0047-655200aEEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.0 aEEGbased brain computer interface BCI Search for optimal electro  c1996  a111–1210 v213 aSeveral laboratories around the world have recently started to investigate EEG-based brain computer interface (BCI) systems in order to create a new communication channel for subjects with severe motor impairments. The present paper describes an initial evaluation of 64-channel EEG data recorded while subjects used one EEG channel over the left sensorimotor area to control on-line vertical cursor movement. Targets were given at the top or bottom of a computer screen. Data from 3 subjects in the early stages of training were analyzed by calculating band power time courses and maps for top and bottom targets separately. In addition, the Distinction Sensitive Learning Vector Quantizer (DSLVQ) was applied to single-trial EEG data. It was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control.10aUser-Computer Interface1 aPfurtscheller, G1 aFlotzinger, D.1 aPregenzer, M.1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/8776708