Multi-objective evolutionary methods for channel selection in Brain-Computer Interfaces: Some preliminary experimental results

Author

Hasan, Bashar Awwad Shiekh ; Gan, John Q. ; Zhang, Qingfu

Author_Institution

Sch. of Comput. Sci. & Electron. Eng., Univ. of Eessex, Colchester, UK

fYear

2010

fDate

18-23 July 2010

Firstpage

1

Lastpage

6

Abstract

This paper presents a comparative study among three evolutionary and search based methods to solve the problem of channel selection for Brain-Computer Interface (BCI) systems. Multi-Objective Particle Swarm Optimization (MOPSO) method is compared to Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) and single objective Sequential Floating Forward Search (SFFS) method. The methods are tested on the first data set for BCI-Competition IV. The results show the usefulness of the multi-objective evolutionary methods in achieving accuracy results similar to the extensive search method with fewer channels and less computational time.

Keywords

brain-computer interfaces; evolutionary computation; particle swarm optimisation; brain-computer interfaces; channel selection; multiobjective evolutionary methods; multiobjective particle swarm optimization; sequential floating forward search; Accuracy; Electroencephalography; Feature extraction; Optimization; Particle swarm optimization; Search problems;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2010 IEEE Congress on

Conference_Location

Barcelona

Print_ISBN

978-1-4244-6909-3

Type

conf

DOI

10.1109/CEC.2010.5586411

Filename

5586411