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Improving Accuracy of Event-Related Potentials Classification by Channel Selection Using Independent Component Analysis and Least Square Methods

Improving Accuracy of Event-Related Potentials Classification by Channel Selection Using Independent Component Analysis and Least Square Methods

Wenxuan Li, Mengfan Li, Wei Li
Copyright: © 2016 |Volume: 8 |Issue: 3 |Pages: 18
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781466690707|DOI: 10.4018/IJSSCI.2016070101
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MLA

Li, Wenxuan, et al. "Improving Accuracy of Event-Related Potentials Classification by Channel Selection Using Independent Component Analysis and Least Square Methods." IJSSCI vol.8, no.3 2016: pp.1-18. http://doi.org/10.4018/IJSSCI.2016070101

APA

Li, W., Li, M., & Li, W. (2016). Improving Accuracy of Event-Related Potentials Classification by Channel Selection Using Independent Component Analysis and Least Square Methods. International Journal of Software Science and Computational Intelligence (IJSSCI), 8(3), 1-18. http://doi.org/10.4018/IJSSCI.2016070101

Chicago

Li, Wenxuan, Mengfan Li, and Wei Li. "Improving Accuracy of Event-Related Potentials Classification by Channel Selection Using Independent Component Analysis and Least Square Methods," International Journal of Software Science and Computational Intelligence (IJSSCI) 8, no.3: 1-18. http://doi.org/10.4018/IJSSCI.2016070101

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Abstract

This paper proposes a method for achieving a high performance of N200 and P300 classification by applying independent component analysis to select the channels, which deliver brain signals with large N200 and P300 potentials and small artifacts. In this study, the authors find out the relationship between the highest accuracy and the weights of the independent components and use this relationship to predict the optimal channels of each individual subject. They compare five channel selection methods: the ICA-based method and the curve-fitting-based method proposed in this paper, the amplitude-based method, the experiential optimal 8 channel combination and all 30 channel combination methods. The comparative studies show that the ICA-based method achieves an average accuracy of 99.3% across four subjects, which is superior to the other four methods.

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