%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 %X

We 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 Book Section %B Pattern Recognition %D 2006 %T Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals. %A Jeremy Jeremy Hill %A Lal, T.N %A Schröder, Michael %A Hinterberger, T. %A Widman, Guido %A Elger, Christian %A Schölkopf, B %A Niels Birbaumer %E Franke, Katrin %E Müller, Klaus-Robert %E Nickolay, Bertram %E Schäfer, Ralf %X

We employed three different brain signal recording methods to perform Brain-Computer Interface studies on untrained subjects. In all cases, we aim to develop a system that could be used for fast, reliable preliminary screening in clinical BCI application, and we are interested in knowing how long screening sessions need to be. Good performance could be achieved, on average, after the first 200 trials in EEG, 75–100 trials in MEG, or 25–50 trials in ECoG. We compare the performance of Independent Component Analysis and the Common Spatial Pattern algorithm in each of the three sensor types, finding that spatial filtering does not help in MEG, helps a little in ECoG, and improves performance a great deal in EEG. In all cases the unsupervised ICA algorithm performed at least as well as the supervised CSP algorithm, which can suffer from poor generalization performance due to overfitting, particularly in ECoG and MEG.

%B Pattern Recognition %S Lecture Notes in Computer Science %I Springer Berlin / Heidelberg %V 4174 %P 404-413 %@ 978-3-540-44412-1 %G eng %U http://dx.doi.org/10.1007/11861898_41 %R 10.1007/11861898_41 %0 Journal Article %D 2005 %T Methods Towards Invasive Human Brain Computer Interfaces. %A Lal, T.N %A Hinterberger, T. %A Widman, Guido %A Schroeder, Michael %A Jeremy Jeremy Hill %A Rosenstiel, W. %A Elger, Christian %A Schölkopf, B %A Niels Birbaumer %K Brain Computer Interfaces %X

During the last ten years there has been growing interest in the develop- ment of Brain Computer Interfaces (BCIs). The field has mainly been driven by the needs of completely paralyzed patients to communicate. With a few exceptions, most human BCIs are based on extracranial elec- troencephalography (EEG). However, reported bit rates are still low. One reason for this is the low signal-to-noise ratio of the EEG [16]. We are currently investigating if BCIs based on electrocorticography (ECoG) are a viable alternative. In this paper we present the method and examples of intracranial EEG recordings of three epilepsy patients with electrode grids placed on the motor cortex. The patients were asked to repeat- edly imagine movements of two kinds, e.g., tongue or finger movements. We analyze the classifiability of the data using Support Vector Machines (SVMs) [18, 21] and Recursive Channel Elimination (RCE) [11]. 

%8 2005 %@ 0-262-19534-8 %G eng %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.8486 %R 10.1.1.64.8486