Frequency component selection for an EEG-based brain to computer interface.

Full Abstract

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.

Author information

Author/s: Pregenzer, M (M); Pfurtscheller, G (G);

Affiliation: Department of Medical Informatics, Institute of Biomedical Engineering. Graz University of Technology and Ludwig-Boltzmann Institute for Medical Informatics and Neuroinformatics, Austria.

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (IEEE Trans Rehabil Eng), published in UNITED STATES. (Language: eng)

Reference: 1999-Dec; vol 7 (issue 4) : pp 413-9

Dates: Created 2000/01/06; Completed 2000/01/06; Revised 2006/11/15;

PMID: 10609628, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: )

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