Paper Info
Title
Using a Multiple Classifier System for Improving the Performance of Asynchronous Brain Interface Systems
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
Year
DOI
Venue
2006
10.1109/ICASSP.2006.1661423
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference
Keywords
Field
DocType
electroencephalography,asynchronous brain interface systems,multiple EEG channels,multiple classifier system
Spatial analysis,Asynchronous communication,Pattern recognition,Computer science,Brain–computer interface,Communication channel,Artificial intelligence,Control system,Classifier (linguistics),Genetic algorithm,Electroencephalography,Machine learning
Conference
Volume
ISSN
ISBN
5
1520-6149
1-4244-0469-X
Citations 
PageRank 
References 
0
0.34
2
Authors
3
Name
Order
Citations
PageRank
Mehrdad Fatourechi116911.96
Gary E. Birch28211.36
Rabab K Ward31440135.88