01983nas 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-temporal03145nas a2200373   4500008004100000022001400041245005700055210005400112260001200166300001000178490000700188520212100195653001002316653001502326653001802341653002102359653003302380653002702413653001302440653002202453653001102475653001102486653000902497653003502506653003102541653003202572100001902604700001902623700001902642700001702661700002402678700002102702856004802723       2008                        eng d  a1095-957200aReal-time detection of event-related brain activity.0 aRealtime detection of eventrelated brain activity  c11/2008  a245-90 v433 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">The complexity and inter-individual variation 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 impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex&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 without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of&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;function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize&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 changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.</span></p>10aAdult10aAlgorithms10aBrain Mapping10aComputer Systems10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aEvoked Potentials10aFemale10aHumans10aMale10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity1 aSchalk, Gerwin1 aLeuthardt, E C1 aBrunner, Peter1 aOjemann, J G1 aGerhardt, Lester, A1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1871854402177nas 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-bci02357nas a2200385   4500008004100000022001400041245009800055210006900153260001200222300001100234490000600245520131000251653001001561653001501571653000801586653001801594653002001612653002701632653002901659653001101688653001101699653000901710653001301719100001901732700001601751700002001767700002601787700001901813700001701832700001601849700001301865700002401878700002101902856004801923       2007                        eng d  a1741-256000aDecoding two-dimensional movement trajectories using electrocorticographic signals in humans.0 aDecoding twodimensional movement trajectories using electrocorti  c09/2007  a264-750 v43 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Signals from the brain could provide a non-muscular communication and control system, a brain-computer interface (BCI), for people who are severely paralyzed. A common BCI research strategy begins by decoding kinematic parameters from brain signals recorded during actual arm movement. It has been assumed that these parameters can be derived accurately only from signals recorded by intracortical microelectrodes, but the long-term stability of such electrodes is uncertain. The present study disproves this widespread assumption by showing in humans that kinematic parameters can also be decoded from signals recorded by subdural electrodes on the cortical surface (ECoG) with an accuracy comparable to that achieved in monkey studies using intracortical microelectrodes. A new ECoG feature labeled the local motor potential (LMP) provided the most information about movement. Furthermore, features displayed cosine tuning that has previously been described only for signals recorded within the brain. These results suggest that ECoG could be a more stable and less invasive alternative to intracortical electrodes for BCI systems, and could also prove useful in studies of motor function.</span></p>10aAdult10aAlgorithms10aArm10aBrain Mapping10aCerebral Cortex10aElectroencephalography10aEvoked Potentials, Motor10aFemale10aHumans10aMale10aMovement1 aSchalk, Gerwin1 aKubánek, J1 aMiller, John, W1 aAnderson, Nicholas, R1 aLeuthardt, E C1 aOjemann, J G1 aLimbrick, D1 aMoran, D1 aGerhardt, Lester, A1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/17873429