Developing a Novel Multi-fusion Brain-Computer Interface (BCI) System with Particle Swarm Optimization for Motor Imagery Task

Tsung-Yu Hsieh, Yang-Yin Lin, Yu-Ting Liu, Chieh-Ning Fang, Chin-Teng Lin

研究成果: Paper同行評審

3 引文 斯高帕斯(Scopus)

摘要

In this paper, we develop a novel multi-fusion brain-computer interface (BCI) based on linear discriminant analysis (LDA) to deal with motor imagery (MI) classification problem. We combine filter bank and sub-band common spatial pattern (SBCSP) to extract features from EEG data in the preprocessing phase, and then LDA classifiers are applied to classify brain activities to identify either left or right hand imagery. To further foster the performance of the proposed system, a fuzzy integral (FI) approach is employed to fuse information sources, and particle swarm optimization (PSO) algorithm is exploited to globally update parameters in the fusion structure. Consequently, our experimental results indicate that the proposed system provides superior performance compared to other approaches.
原文English
DOIs
出版狀態Published - 2015

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