Wavelet-coherence Features for Motor Imagery Eeg Analysis Posterior to Eog Noise Elimination
نویسندگان
چکیده
In this study, we propose a brain-computer interface (BCI) system to analyze single-trial electroencephalogram (EEG) signals. After the automatic EOG-artifact elimination, wavelet-coherence features and support vector machine (SVM) are adopted for the classification of left and right motor imagery (MI) data. EOG artifacts are removed automatically via modified independent component analysis (ICA). The features are extracted from wavelet data by means of coherence, and then classified by the SVM. Compared with EEG data without EOG artifact removal, spectral band and AR model features, the proposed system achieves satisfactory results in BCI applications.
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