Independent component analysis-based channel selection to achieve high performance of N200 and P300 classification

Wenxuan Li; Mengfan Li; Wei Li

doi:10.1109/ICCI-CC.2015.7259414

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

Independent component analysis-based channel selection to achieve high performance of N200 and P300 classification

Year: 2015, Pages: 384-389

DOI Bookmark: 10.1109/ICCI-CC.2015.7259414

Authors

Wenxuan Li, School of Electrical Engineering and Automation, Tianjin University, China
Mengfan Li, School of Electrical Engineering and Automation, Tianjin University, China
Wei Li, School of Electrical Engineering and Automation, Tianjin University, China

Abstract

This paper proposes a method for achieving a high performance of N200 and P300 classification, which applies independent component analysis (ICA) to select the channels whose brain signals contain large N200 and P300 potentials and small artifacts as the optimal channels to extract the features. The study results show that our method achieves an average accuracy of 99.3% over 4 subjects.

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Independent component analysis-based channel selection to achieve high performance of N200 and P300 classification

Authors

Abstract

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