关键词: 相空间重构, 非线性几何特征, 非线性全局特征, 特征融合

Abstract: In view of the imperfection of the existing nonlinear characteristics of EEG signals which represent emotional information?, this paper introduces phase space reconstruction theory into the feature extraction of emotional EEG signals, three nonlinear geometric features of trajectory-based descriptive contours under the reconstructed phase space are extracted as the new emotional EEG characteristic parameters. Combining the power spectrum entropy and nonlinear attribute feature（approximate entropy, maximum Lyapunov exponent, Hurst exponent） of EEG signals, a nonlinear global feature（nonlinear geometric feature + nonlinear attribute feature） and power spectrum entropy fusion algorithm based on Principal Component Analysis（PCA） is proposed, Support Vector Machine（SVM） is employed to classify for emotion recognition. The results show that the nonlinear global feature can realize emotion recognition more effectively, and the rate of two-classification emotion recognition is about 90%. ?The fusion emotion feature based on PCA can achieve better emotion recognition performance than a single feature, and the average recognition rate can reach 86.42% in four classification experiments. The results show that the recognition rate of nonlinear global feature is higher than that of nonlinear attribute feature, and the combination of nonlinear global feature and power spectrum entropy can construct better emotional EEG feature parameters.

Key words: phase space reconstruction, nonlinear geometric features, nonlinear global feature, feature fusion