| Paper: | MLSP-P6.8 | ||
| Session: | Biomedical and Other Applications | ||
| Time: | Friday, May 19, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Biomedical Applications and Neural Engineering | ||
| Title: | USING A MULTIPLE CLASSIFIER SYSTEM FOR IMPROVING THE PERFORMANCE OF ASYNCHRONOUS BRAIN INTERFACE SYSTEMS | ||
| Authors: | Mehrdad Fatourechi, Gary E. Birch, Rabab K. Ward, University of British Columbia, Canada | ||
| Abstract: | To improve the performance of asynchronous brain interface (ABI) systems, a new classifier design is proposed. The spatial information of multiple EEG channels data is first used to create independent classifiers for different channels. A subset of these classifiers is then selected by a genetic algorithm to form a multiple classifier system (MCS) to decide whether a trial is an intended control or a no control signal. The analysis of the data from 4 subjects shows the effectiveness of the proposed method in improving the performance of an ABI system compared to the results obtained using only the best performing channel. | ||
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