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

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