%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 Journal Article %J Neurology %D 2005 %T Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. %A Kübler, A. %A Nijboer, F %A Mellinger, Jürgen %A Theresa M Vaughan %A Pawelzik, H %A Gerwin Schalk %A Dennis J. McFarland %A Niels Birbaumer %A Jonathan Wolpaw %K Aged %K Amyotrophic Lateral Sclerosis %K Electroencephalography %K Evoked Potentials, Motor %K Evoked Potentials, Somatosensory %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Motor Cortex %K Movement %K Paralysis %K Photic Stimulation %K Prostheses and Implants %K Somatosensory Cortex %K Treatment Outcome %K User-Computer Interface %X

People with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS.

%B Neurology %V 64 %P 1775-7 %8 05/2005 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15911809 %N 10 %R 10.1212/01.WNL.0000158616.43002.6D