Cultural-Based Multi-Objective Particle Swarm Optimization for EEG Channel Reduction in Multi-Class Brain-Computer Interfaces

Qing Guo Wei; Yan Mei Wang; Zong Wu Lu

doi:10.4028/www.scientific.net/AMM.239-240.1027

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 239-240Cultural-Based Multi-Objective Particle Swarm...

Cultural-Based Multi-Objective Particle Swarm Optimization for EEG Channel Reduction in Multi-Class Brain-Computer Interfaces

Abstract:

Applying many electrodes is undesirable for real-life brain-computer interface (BCI) application since the recording preparation can be troublesome and time-consuming. Multi-objective particle swarm optimization (MOPSO) has been widely utilized to solve multi-objective optimization problems and thus can be employed for channel selection. This paper presented a novel method named cultural-based MOPSO (CMOPSO) for channel selection in motor imagery based BCI. The CMOPSO method introduces a cultural framework to adapt the personalized flight parameters of the mutated particles. A comparison between the proposed algorithm and typical L1-norm algorithm was conducted, and the results showed that the proposed approach is more effective in selecting a smaller subset of channels while maintaining the classification accuracy unreduced.

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Periodical:

Applied Mechanics and Materials (Volumes 239-240)

Pages:

1027-1032

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.239-240.1027

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Online since:

December 2012

Authors:

Qing Guo Wei, Yan Mei Wang, Zong Wu Lu

Keywords:

Brain Computer Interface (BCI), Channel Selection, Common Spatial Pattern, Cultural-Based Multi-Objective Particle Swarm Optimization

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