Research on feature extraction algorithms in BCI

Authors:
Yuge Sun;Ning Ye;Lihong Zhao;Xinhe Xu
Affiliations:
College of Information Science and Engineering, Northeastern University, Shenyang;College of Information Science and Engineering, Northeastern University, Shenyang;College of Information Science and Engineering, Northeastern University, Shenyang;College of Information Science and Engineering, Northeastern University, Shenyang
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Subject-based feature extraction using fuzzy wavelet packet in brain-computer interfaces

Signal Processing

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Abstract

In this paper, wavelet packet algorithm, wavelet entropy algorithm and AR model algorithm were investigated for feature extraction. EEG data of six subjects were analyzed while they performed five different mental tasks. Based on the recognition rate under different mental EEG combination and different subject, it proved that wavelet entropy algorithm had better classification accuracy compared with the other two algorithms. The highest recognition rate is up to 98.48%. The research is valuable and significant in the realization of control and communication based on the mental tasks in BCI.