A Common Spatial Pattern and Wavelet Packet Decomposition Combined Method for EEG-Based Emotion Recognition
Jingxia Chen*,**,, Dongmei Jiang*, and Yanning Zhang*
*School of Computer Science and Engineering, Northwestern Polytechnical University
Xi’an, Shaanxi 710072, China
**Department of Electrical and Information Engineering, Shaanxi University of Science and Technology
Xi’an, Shaanxi 710021, China
To effectively reduce the day-to-day fluctuations and differences in subjects’ brain electroencephalogram (EEG) signals and improve the accuracy and stability of EEG emotion classification, a new EEG feature extraction method based on common spatial pattern (CSP) and wavelet packet decomposition (WPD) is proposed. For the five-day emotion related EEG data of 12 subjects, the CSP algorithm is firstly used to project the raw EEG data into an optimal subspace to extract the discriminative features by maximizing the Kullback-Leibler (KL) divergences between the two categories of EEG data. Then the WPD algorithm is used to decompose the EEG signals into the related features in time-frequency domain. Finally, four state-of-the-art classifiers including Bagging tree, SVM, linear discriminant analysis and Bayesian linear discriminant analysis are used to make binary emotion classification. The experimental results show that with CSP spatial filtering, the emotion classification on the WPD features extracted with bior3.3 wavelet base gets the best accuracy of 0.862, which is 29.3% higher than that of the power spectral density (PSD) feature without CSP preprocessing, is 23% higher than that of the PSD feature with CSP preprocessing, is 1.9% higher than that of the WPD feature extracted with bior3.3 wavelet base without CSP preprocessing, and is 3.2% higher than that of the WPD feature extracted with the rbio6.8 wavelet base without CSP preprocessing. Our proposed method can effectively reduce the variance and non-stationary of the cross-day EEG signals, extract the emotion related features and improve the accuracy and stability of the cross-day EEG emotion classification. It is valuable for the development of robust emotional brain-computer interface applications.
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