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

► Automatic EOG artifact removal is proposed to eliminate the EOG artifacts by means of ICA and correlation coefficient. ► The features are extracted from wavelet transform data by amplitude modulation method. ► The SVM is used for the discrimination of wavelet-based AM features. ► The proposed method compares with EEG data without EOG artifact removal, band power features and LDA classifier. 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.