基于脑电信号的情感识别研究

he relationship between EEG and emotion recognition has attracted wide attention, however, the partial accuracy of emotion recognition is low. For improving the accuracy rate, after filtered the original EEG signal to 5 bands, this paper then extracted 4 features, the differential entropy, power spectral density, wavelet entropy and approximate entropy. Finally selected the features and used the support vector machine, k-nearest neighbor, naive Bayesian model and multi-layer perceptron for classification learning. The paper trained DE feature to get the higher accuracy with 4 different electrode placement methods by Support vector machine, moreover, the accuracy rate reach 91.99% of all EEG band energy in the case of 12 channels. Whats more, we get the average accuracies up to 97.59% with SVM that using genetic algorithm to acquire the optimization parameters.