Paper Info
Title
A Binary Bees Algorithm for P300-Based Brain-Computer Interfaces Channel Selection.
Abstract
Brain-computer interface (BCI) systems need to work in real-time with large amounts of data, which makes the channel selection procedures essential to reduce over-fitting and to increase users' comfort. In that sense, metaheuristics based on swarm intelligence (SI) have demonstrated excellent performances solving complex optimization problems and, to the best of our knowledge, they have not been fully exploited in P300-BCI systems. In this study, we propose a modified SI method, called binary bees algorithm (b-BA), that allows users to select the most relevant channels in an evolutionary way. This method has been compared to particle swarm optimization (PSO) and tested with the 'III BCI Competition 2005' dataset II. Results show that b-BA is suitable for use in this kind of systems, reaching higher accuracies (mean of 96.0 +/- 0.0%) than PSO (mean of 93.5 +/- 2.1%) and the original ones (mean of 94.0 +/- 2.8%) using less than the half of the initial channels.
Year
DOI
Venue
2017
10.1007/978-3-319-59147-6_39
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II
Keywords
Field
DocType
Brain-computer interfaces,P300 evoked potentials,Channel selection,Swarm intelligence,Bees algorithm,Particle swarm optimization
Particle swarm optimization,Pattern recognition,Computer science,Brain–computer interface,Swarm intelligence,Communication channel,Algorithm,Artificial intelligence,Bees algorithm,Optimization problem,Metaheuristic,Binary number
Conference
Volume
ISSN
Citations 
10306
0302-9743
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Víctor Martínez-Cagigal133.11
Roberto Hornero260367.74