Paper Info
Title
A Hybrid Genetic Algorithm Approach For Improving The Performance Of The Lf-Asd Brain Computer Interface
Abstract
An asynchronous Brain Computer Interface (BCI) continuously monitors the brain signals and is activated only when a user intends control. Initial results from an asynchronous system, the LF-ASD, designed by our group have shown promise, but the reported error rates are still high for most practical applications. To improve its performance, we propose user customization. Since energy normalization of all channels' signals is shown to significantly improve the performance of the system, we choose to customize the parameters related to this normalization. We apply a hybrid Genetic Algorithm (a Genetic Algorithm followed by a Local Search) to customize the size of the energy normalization windows. This is shown to significantly improve the results. For a fixed false positive rate of 2%, the improvement in the true positive rate was raised from 65.7% to 76.9% in one subject and from 53.1% to 63.3% for another subject.
Year
DOI
Venue
2005
10.1109/ICASSP.2005.1416311
2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING
Keywords
Field
DocType
bci,error rate,genetic algorithm,human computer interaction,local search,biocontrol,brain computer interface,space exploration,asynchronous system,genetic algorithms,brain computer interfaces,feature extraction,control systems,switches,performance,false positive rate
False positive rate,Asynchronous communication,Normalization (statistics),Asynchronous system,Computer science,Brain–computer interface,Speech recognition,Control system,Local search (optimization),Genetic algorithm
Conference
ISSN
Citations 
PageRank 
1520-6149
7
0.70
References 
Authors
2
4
Name
Order
Citations
PageRank
Mehrdad Fatourechi116911.96
Ali Bashashati2523.86
Rabab K Ward31440135.88
Gary E. Birch48211.36