@proceedings {2241, title = {Detection of spontaneous class-specific visual stimuli with high temporal accuracy in human electrocorticography.}, volume = {2009}, year = {2009}, month = {2009}, pages = {6465-8}, abstract = {Most 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.}, keywords = {Algorithms, Electrocardiography, Evoked Potentials, Visual, Humans, Male, Pattern Recognition, Automated, Pattern Recognition, Visual, Photic Stimulation, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface, Visual Cortex}, issn = {1557-170X}, doi = {10.1109/IEMBS.2009.5333546}, author = {Miller, John W and Hermes, Dora and Gerwin Schalk and Ramsey, Nick F and Jagadeesh, Bharathi and den Nijs, Marcel and Ojemann, J G and Rao, Rajesh P N} } @article {2187, title = {Real-time detection of event-related brain activity.}, journal = {Neuroimage}, volume = {43}, year = {2008}, month = {11/2008}, pages = {245-9}, abstract = {

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

}, keywords = {Adult, Algorithms, Brain Mapping, Computer Systems, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Evoked Potentials, Female, Humans, Male, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2008.07.037}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18718544}, author = {Gerwin Schalk and Leuthardt, E C and Peter Brunner and Ojemann, J G and Lester A Gerhardt and Jonathan Wolpaw} } @proceedings {2240, title = {Three cases of feature correlation in an electrocorticographic BCI.}, year = {2008}, month = {2008}, pages = {5318-21}, abstract = {Three 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.}, keywords = {Adolescent, Adult, Algorithms, Electrocardiography, Evoked Potentials, Motor, Female, Humans, Male, Middle Aged, Motor Cortex, Pattern Recognition, Automated, Statistics as Topic, Task Performance and Analysis, User-Computer Interface}, issn = {1557-170X}, doi = {10.1109/IEMBS.2008.4650415}, author = {Miller, John W and Blakely, Timothy and Gerwin Schalk and den Nijs, Marcel and Rao, Rajesh P N and Ojemann, J G} } @article {2182, title = {Decoding two-dimensional movement trajectories using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {4}, year = {2007}, month = {09/2007}, pages = {264-75}, abstract = {

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.

}, keywords = {Adult, Algorithms, Arm, Brain Mapping, Cerebral Cortex, Electroencephalography, Evoked Potentials, Motor, Female, Humans, Male, Movement}, issn = {1741-2560}, doi = {10.1088/1741-2560/4/3/012}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17873429}, author = {Gerwin Schalk and Kub{\'a}nek, J and Miller, John W and Nicholas R Anderson and Leuthardt, E C and Ojemann, J G and Limbrick, D and Moran, D and Lester A Gerhardt and Jonathan Wolpaw} }