Using a Multiple Classifier System for Improving the Performance of Asynchronous Brain Interface Systems

Author

Fatourechi, Mehrdad ; Birch, Gary E. ; Ward, Rabab K.

Author_Institution

Dept. of Electr. & Comput. Eng., British Columbia Univ., Vancouver, BC

Volume

5

fYear

2006

fDate

14-19 May 2006

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

Keywords

electroencephalography; asynchronous brain interface systems; multiple EEG channels; multiple classifier system; Bismuth; Brain computer interfaces; Communication system control; Control systems; Data analysis; Electroencephalography; Genetic algorithms; Performance analysis; Switches; Wheelchairs;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on

Conference_Location

Toulouse

ISSN

1520-6149

Print_ISBN

1-4244-0469-X

Type

conf

DOI

10.1109/ICASSP.2006.1661423

Filename

1661423