An efficient p300-based BCI using wavelet features and IBPSO-based channel selection

Abstract

We present a novel and efficient scheme that selects a minimal set of effective features and channels for detecting the P300 component of the event-related potential in the brain-computer interface (BCI) paradigm. For obtaining a minimal set of effective features, we take the truncated coefficients of discrete Daubechies 4 wavelet, and for selecting the effective electroencephalogram channels, we utilize an improved binary particle swarm optimization algorithm together with the Bhattacharyya criterion. We tested our proposed scheme on dataset IIb of BCI competition 2005 and achieved 97.5% and 74.5% accuracy in 15 and 5 trials, respectively, using a simple classification algorithm based on Bayesian linear discriminant analysis. We also tested our proposed scheme on Hoffmann′s dataset for eight subjects, and achieved similar results.

Keywords

• Bayesian linear discriminant analysis
• Bhattacharyya distance
• brain-computer interface
• discrete wavelet
• event-related potentials
• improved binary particle swarm optimization algorithm