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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Reducing the Computation Time for BCI Using Improved ICA Algorithms
verfasst von : Lu Huang, Hong Wang
Erschienen in: Advances in Neural Networks – ISNN 2013
Verlag: Springer Berlin Heidelberg
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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.