Reducing the computation time for BCI using improved ICA algorithms

Authors:
Lu Huang;Hong Wang
Affiliations:
Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China,College of Information Engineering, Dalian Ocean University, Dalian, China;Northeastern University, Shenyang, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

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Abstract

P300 is a popular characteristic potential for electroencephalogram(EEG) based brain-computer interface(BCI). In P300-BCI, the extraction of P300 is a very crucial operation. Independent component analysis(ICA) technique is suitable for P300 extraction. In this paper, aiming at the current large volume of EEG data, the applications of three ICA algorithms were proposed for P300 extraction and were compared. The experiments ran on real EEG data respectively. PI and recognition accuracy were checked. The results show artificial fish swarm algorithm based ICA(AFSA_ICA) can extract P300 faster, reducing the computation time for BCI with PI remaining better.