Optimal frequency band selection with particle swarm optimization for a brain computer interface

Recent research has shown that selecting specific frequency bands for the extraction of invariant characteristics specific to each brain state can significantly improve the performance and accuracy of a brain-computer interface (BCI). Subject-specific frequency band selection helps in filtering out the nonevent related data, hence, assists in concentrating on the band where the separability between different classes is maximum. On the other hand, the subject-specific frequency band selection adds a new parameter to be tuned explicit to each subject which is a limitation for an autonomous BCI system. To realize a BCI system it is desirable to have an algorithm to govern the frequency band selection. This paper presents Particle Swarm Optimization (PSO) as a search algorithm to identify subject-specific frequency bands. A modified PSO method is proposed utilizing two independent swarms of particles in order to identify frequency bands which are maximally separable between two classes of mental tasks and thus enhance the classification of the BCI. Experimental results obtained utilizing BCI competition III data are presented. The results are promising and indicate that particle swarm optimization-based approach presents superior performance and quick convergence, therefore, not only improves the performance but also speeds up the processing time by a factor of between 4-8, compared to a thorough heuristic search for the separable frequency bands.