Frequency component selection for an EEG-based brain to computer interface.
A new communication channel for severely handicapped people could be opened with a direct brain to computer interface (BCI). Such a system classifies electrical brain signals online. In a series of training sessions, where electroencephalograph (EEG) signals are recorded on the intact scalp, a classifier is trained to discriminate a limited number of different brain states. In a subsequent series of feedback sessions, where the subject is confronted with the classification results, the subject tries to reduce the number of misclassifications. In this study the relevance of different spectral components is analyzed: 1) on the training sessions to select optimal frequency bands for the feedback sessions and 2) on the feedback sessions to monitor changes.
Pregenzer
M
M
Department of Medical Informatics, Institute of Biomedical Engineering. Graz University of Technology and Ludwig-Boltzmann Institute for Medical Informatics and Neuroinformatics, Austria.
Pfurtscheller
G
G
D000328:Adult; D000465:Algorithms; D015982:Bias; D001676:Biofeedback, Psychology / Q000379:methods*; D001921:Brain / Q000502:physiology*; D002965:Classification; D003143:Communication Devices for People with Disabilities; D003147:Communication Disorders / Q000209:etiology / Q000503:physiopathology / Q000534:rehabilitation*; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D009862:Online Systems / Q000458:organization & administration*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D014584:User-Computer Interface