Independent component analysis approach for the neurobiological recording data

Authors:
Minghai Yao;Jing Hu;Qinglong Gu
Affiliations:
College of Information Engineering, Zhejiang University of Technology, Hangzhou, China;College of Information Engineering, Zhejiang University of Technology, Hangzhou, China;College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
Venue:
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
Year:
2006

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Independent component analysis: algorithms and applications

Neural Networks

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Abstract

Electroencephalograms (EEG) can provide a unique window on the human brain. Since contamination of EEG recording with artifacts (e.g., signals caused by muscular activity, eye movements, cardiac rhythm and power noise etc.) can decrease the efficiency of diagnosis procedure, here we apply a kind of fast independent component analysis (ICA) approach to analyze the real multi-variant EEG recorded signals. Besides, the comparison between ICA and a second order statistical algorithm are given in this paper. By the real measured data, our experimental results confirm the validity and usefulness of ICA algorithm.