Wavelet-based envelope features with automatic EOG artifact removal: Application to single-trial EEG data

Authors:
Wei-Yen Hsu;Chao-Hung Lin;Hsien-Jen Hsu;Po-Hsun Chen;I-Ru Chen
Affiliations:
Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wu-Xin Street, Taipei 110, Taiwan;Department of Geomatics, National Cheng Kung University No.1, University Road, Tainan City 701, Taiwan;Institute of Manufacturing Engineering, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan;Department of Electrical Engineering, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan;Department of Education, National Chiayi University, No.300, Syuefu Road, Chiayi City 60004, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

In this study, we propose an analysis system for single-trial classification of electroencephalogram (EEG) data. Combined with automatic EOG artifact removal and wavelet-based amplitude modulation (AM) features, the support vector machine (SVM) classifier is applied to the classification of left finger lifting and resting. Automatic EOG artifact removal is proposed to eliminate the EOG artifacts automatically by means of independent component analysis (ICA) and correlation coefficient. The features are then extracted from the discrete wavelet transform (DWT) data by the AM method. Finally, the SVM is used for the discriminant of wavelet-based AM features. Compared with EEG data without EOG artifact removal, band power features and LDA classifier, the proposed system achieves promising results in classification accuracy.