基于脑电信号的情感识别研究

针对如何提高脑电信号情感识别的正确率这一问题，在得到的原始脑电信号进行分频带特征提取后，一方面采用支持向量机、K近邻算法、朴素贝叶斯和神经网络算法对小波熵、近似熵、功率谱密度、微分熵，进行训练和分类学习；另一方面，基于四种不同的电极放置方式，对微分熵特征采用支持向量机和经遗传算法参数寻优的支持向量机算法进行训练。结果显示，在12通道条件下能够得到91.99%的总体准确率，最高情感识别准确率已经达到97.59%。研究结果表明，减少电极可以获得较高的情感识别分类结果，并且采用参数寻优后的支持向量机算法能够有效提升准确率。

Research on emotion recognition based on EEG signals

The 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 it selected the features and used the support vector machine, K-nearest neighbor, naive Bayesian model and multi-layer perceptron for classification learning. It 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. What''s more, this paper gets the average accuracies up to 97.59% with SVM that using genetic algorithm to acquire the optimization parameters.