%0 Journal Article %J Epilepsy Behav %D 2008 %T Voluntary brain regulation and communication with electrocorticogram signals. %A Hinterberger, T. %A Widman, Guido %A Lal, T.N %A Jeremy Jeremy Hill %A Tangermann, Michael %A Rosenstiel, W. %A Schölkopf, B %A Elger, Christian %A Niels Birbaumer %K Adult %K Biofeedback, Psychology %K Cerebral Cortex %K Communication Aids for Disabled %K Dominance, Cerebral %K Electroencephalography %K Epilepsies, Partial %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Motor Activity %K Motor Cortex %K Signal Processing, Computer-Assisted %K Software %K Somatosensory Cortex %K Theta Rhythm %K User-Computer Interface %K Writing %X
Brain-computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain 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 computer-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.
%B Epilepsy Behav %V 13 %P 300-6 %8 08/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/18495541 %N 2 %R 10.1016/j.yebeh.2008.03.014 %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2006 %T Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. %A Jeremy Jeremy Hill %A Lal, T.N %A Schröder, Michael %A Hinterberger, T. %A Wilhelm, Barbara %A Nijboer, F %A Mochty, Ursula %A Widman, Guido %A Elger, Christian %A Schölkopf, B %A Kübler, A. %A Niels Birbaumer %K Algorithms %K Artificial Intelligence %K Cluster Analysis %K Computer User Training %K Electroencephalography %K Evoked Potentials %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Paralysis %K Pattern Recognition, Automated %K User-Computer Interface %XWe summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface 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.
%B IEEE Trans Neural Syst Rehabil Eng %V 14 %P 183-6 %8 06/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16792289 %N 2 %R 10.1109/TNSRE.2006.875548 %0 Journal Article %J EURASIP Journal on Advances in Signal Processing %D 2005 %T Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces. %A Schröder, Michael %A Lal, T.N %A Hinterberger, T. %A Bogdan, Martin %A Jeremy Jeremy Hill %A Niels Birbaumer %A Rosenstiel, W. %A Schölkopf, B %E Vesin J M, T EbrahimiEditor %K brain-computer interface %K channel selection %K Electroencephalography %K feature selection %K recursive channel elimination %K support vector machine %XMost EEG-based brain-computer interface (BCI) paradigms come along with specific electrode positions, for example, for a visual-based BCI, electrode positions close to the primary visual cortex are used. For new BCI paradigms it is usually not known where task relevant activity can be measured from the scalp. For individual subjects, Lal et al. in 2004 showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extent their method of recursive channel elimination (RCE) can be generalized across subjects. In this paper we transfer channel rankings from a group of subjects to a new subject. For motor imagery tasks the results are promising, although cross-subject channel selection does not quite achieve the performance of channel selection on data of single subjects. Although the RCE method was not provided with prior knowledge about the mental task, channels that are well known to be important (from a physiological point of view) were consistently selected whereas task-irrelevant channels were reliably disregarded.