01554nas a2200301   4500008004100000022001400041245006600055210006300121260001200184300001400196490000700210520064700217653001800864653001700882653001300899100002300912700001800935700003000953700002200983700002501005700002501030700001901055700001901074700002401093700001701117700001901134856009901153       2023                        eng d  a1546-172600aA motor association area in the depths of the central sulcus.0 amotor association area in the depths of the central sulcus  c07/2023  a1165-11690 v263 a<p>Cells in the precentral gyrus directly send signals to the periphery to generate movement and are principally organized as a topological map of the body. We find that movement-induced electrophysiological responses from depth electrodes extend this map three-dimensionally throughout the gyrus. Unexpectedly, this organization is interrupted by a previously undescribed motor association area in the depths of the midlateral aspect of the central sulcus. This 'Rolandic motor association' (RMA) area is active during movements of different body parts from both sides of the body and may be important for coordinating complex behaviors.</p>10aBrain Mapping10aMotor Cortex10aMovement1 aJensen, Michael, A1 aHuang, Harvey1 aValencia, Gabriela, Ojeda1 aKlassen, Bryan, T1 avan den Boom, Max, A1 aKaufmann, Timothy, J1 aSchalk, Gerwin1 aBrunner, Peter1 aWorrell, Gregory, A1 aHermes, Dora1 aMiller, Kai, J  uhttps://www.neurotechcenter.org/publications/2023/motor-association-area-depths-central-sulcus02056nas a2200313   4500008004100000022001400041245016300055210006900218260001200287300001100299490000700310520100100317100001901318700002001337700001801357700002301375700001601398700002401414700001701438700002201455700002101477700002001498700002201518700001901540700001901559700002401578700001901602856012101621       2022                        eng d  a1662-454800aToward a fully implantable ecosystem for adaptive neuromodulation in humans: Preliminary experience with the CorTec BrainInterchange device in a canine model.0 aToward a fully implantable ecosystem for adaptive neuromodulatio  c12/2022  a9327820 v163 a<p>This article describes initial work toward an ecosystem for adaptive neuromodulation in humans by documenting the experience of implanting CorTec's BrainInterchange (BIC) device in a beagle canine and using the BCI2000 environment to interact with the BIC device. It begins with laying out the substantial opportunity presented by a useful, easy-to-use, and widely available hardware/software ecosystem in the current landscape of the field of adaptive neuromodulation, and then describes experience with implantation, software integration, and post-surgical validation of recording of brain signals and implant parameters. Initial experience suggests that the hardware capabilities of the BIC device are fully supported by BCI2000, and that the BIC/BCI2000 device can record and process brain signals during free behavior. With further development and validation, the BIC/BCI2000 ecosystem could become an important tool for research into new adaptive neuromodulation protocols in humans.</p>1 aSchalk, Gerwin1 aWorrell, Samuel1 aMivalt, Filip1 aBelsten, Alexander1 aKim, Inyong1 aMorris, Jonathan, M1 aHermes, Dora1 aKlassen, Bryan, T1 aStaff, Nathan, P1 aMessina, Steven1 aKaufmann, Timothy1 aRickert, Jörn1 aBrunner, Peter1 aWorrell, Gregory, A1 aMiller, Kai, J  uhttps://www.neurotechcenter.org/publications/2022/toward-fully-implantable-ecosystem-adaptive-neuromodulation-humans02753nas a2200373   4500008004100000022001400041245011800055210006900173260001200242300001100254490000800265520163200273653001001905653001501915653001701930653002001947653001501967653002501982653001102007653001702018653001102035653000902046653001602055653001402071653002802085653001802113653001602131100003202147700002102179700001902200700001902219700001902238856012202257       2021                        eng d  a1095-957200aWithin-subject reaction time variability: Role of cortical networks and underlying neurophysiological mechanisms.0 aWithinsubject reaction time variability Role of cortical network  c08/2021  a1181270 v2373 a<p>Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Using a previously described technique that can identify the onset of population-level cortical activity and a novel functional connectivity algorithm described herein, we show that the cumulative latency difference of population-level neural activity across the task-related cortical network can explain up to 41% of the trial-by-trial variance in reaction time. Furthermore, we show that reaction time variance may primarily be due to the latencies in specific brain regions and demonstrate that behavioral latency variance is accumulated across the whole task-related cortical network. Our results suggest that population-level neural activity monotonically increases prior to movement execution, and that trial-by-trial changes in that increase are, in part, accounted for by inhibitory activity indexed by low-frequency oscillations. This pre-movement neural activity explains 19% of the measured variance in neural latencies in our data. Thus, our study provides a mechanistic explanation for a sizable fraction of behavioral reaction time when the subject's task is the same from trial to trial.</p>10aAdult10aAlgorithms10aAlpha Rhythm10aCerebral Cortex10aConnectome10aElectrocorticography10aFemale10aGamma Rhythm10aHumans10aMale10aMiddle Aged10aNerve Net10aPsychomotor Performance10aReaction Time10aYoung Adult1 aParaskevopoulou, Sivylla, E1 aCoon, William, G1 aBrunner, Peter1 aMiller, Kai, J1 aSchalk, Gerwin  uhttps://www.neurotechcenter.org/publications/2021/within-subject-reaction-time-variability-role-cortical-networks-and02134nas a2200253   4500008004100000020002200041022002200063245009200085210006900177260005700246300001200303520128900315653002001604653002501624653002801649653002001677653001701697100001901714700001901733700001701752700002401769700002101793856006601814       2015                        eng d  a978-3-319-25188-2  a978-3-319-25190-500aNear-Instantaneous Classification of Perceptual States from Cortical Surface Recordings0 aNearInstantaneous Classification of Perceptual States from Corti  aNew York City, NYbSpringer International Publishing  a105-1143 aHuman visual processing is of such complexity that, despite decades of focused research, many basic questions remain unanswered. Although we know that the inferotemporal cortex is a key region in object recognition, we don’t fully understand its physiologic role in brain function, nor do we have the full set of tools to explore this question. Here we show that electrical potentials from the surface of the human brain contain enough information to decode a subject’s perceptual state accurately, and with fine temporal precision. Electrocorticographic (ECoG) arrays were placed over the inferotemporal cortical areas of seven subjects. Pictures of faces and houses were quickly presented while each subject performed a simple visual task. Results showed that two well-known types of brain signals—event-averaged broadband power and event-averaged raw potential—can independently or together be used to classify the presented image. When applied to continuously recorded brain activity, our decoding technique could accurately predict whether each stimulus was a face, house, or neither, with  20 ms timing error. These results provide a roadmap for improved brain-computer interfacing tools to help neurosurgeons, research scientists, engineers, and, ultimately, patients.10abroadband power10aElectrocorticography10aevent-related potential10afusiform cortex10ahuman vision1 aMiller, Kai, J1 aSchalk, Gerwin1 aHermes, Dora1 aOjemann, Jeffrey, G1 aRao, Rajesh, P N  uhttp://link.springer.com/chapter/10.1007/978-3-319-25190-5_1002352nas 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/19163918