02134nas a2200253   4500008004100000020002200041022002200063245009200085210006900177260005700246300001200303520128900315653002001604653002501624653002801649653002001677653001701697100001901714700001901733700001701752700002401769700002101793856006601814       2015                        eng d  a978-3-319-25188-2  a978-3-319-25190-500aNear-Instantaneous Classification of Perceptual States from Cortical Surface Recordings0 aNearInstantaneous Classification of Perceptual States from Corti  aNew York City, NYbSpringer International Publishing  a105-1143 aHuman visual processing is of such complexity that, despite decades of focused research, many basic questions remain unanswered. Although we know that the inferotemporal cortex is a key region in object recognition, we don’t fully understand its physiologic role in brain function, nor do we have the full set of tools to explore this question. Here we show that electrical potentials from the surface of the human brain contain enough information to decode a subject’s perceptual state accurately, and with fine temporal precision. Electrocorticographic (ECoG) arrays were placed over the inferotemporal cortical areas of seven subjects. Pictures of faces and houses were quickly presented while each subject performed a simple visual task. Results showed that two well-known types of brain signals—event-averaged broadband power and event-averaged raw potential—can independently or together be used to classify the presented image. When applied to continuously recorded brain activity, our decoding technique could accurately predict whether each stimulus was a face, house, or neither, with  20 ms timing error. These results provide a roadmap for improved brain-computer interfacing tools to help neurosurgeons, research scientists, engineers, and, ultimately, patients.10abroadband power10aElectrocorticography10aevent-related potential10afusiform cortex10ahuman vision1 aMiller, Kai, J1 aSchalk, Gerwin1 aHermes, Dora1 aOjemann, Jeffrey, G1 aRao, Rajesh, P N  uhttp://link.springer.com/chapter/10.1007/978-3-319-25190-5_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