关键词: 相关向量机, 支持向量机, 多分类, 脑电信号, 情感识别

Abstract: Based on the similarity of Relevance Vector Machine（RVM） and Support Vector Machine（SVM） and the sparse characteristics of RVM, RVM is applied to the electroencephalogram（EEG） signal emotional recognition. Aiming at the characteristics and deficiencies of the two methods of One-Against-One（OAO） and One-Against-All（OAA）, this paper proposes a new two-layer multi-classification model（OAA-OAO） to improve the effect of invalid voting on the final decision-making in the existing OAO algorithm. A series of emotional EEG signal recognition experiments are designed to verify the effectiveness of improved multi-classification algorithm based on RVM in EEG emotion recognition. For the emotional EEG signal collected by the laboratory, this paper extracts the nonlinear features（power spectral entropy, sample entropy and Hurst index） and reduces the dimension by Principal Component Analysis（PCA）. OAA-OAO-RVM is compared with OAO-SVM and OAO-RVM respectively, the recognition performance of RVM and the classification performance of OAA-OAO is analyzed. The results show that using the optimal feature set after dimension reduction as input vector of the recognition network has higher recognition rate, and the performance of RVM is better than that of SVM. At the same time, the average recognition rate of OAA-OAO is 7.89% higher than that of OAO, which proves that the OAA-OAO algorithm can effectively remove part of the invalid votes so that the classification accuracy can be improved obviously and this model is an effective multi-classification model.

Key words: Relevance Vector Machine（RVM）, Support Vector Machine（SVM）, multi-classification, electroencephalogram（EEG） signal, emotional recognition