Multi-reservoirs EEG signal feature sensing and recognition method based on generative adversarial networks

Abstract

EEG emotion recognition is one of the interesting and challenging tasks in the research based emotion human–computer interface system. In this paper, a multi-reservoirs feature coding continuous label fusion semi-supervised Generative Adversarial Networks (MCLFS-GAN) is proposed by using permutation phase transfer entropy as the EEG signal feature. Firstly, the obtained features are encapsulated in time series, and then the features are sent into multi-reservoirs according to the division of brain intra, brain interval or frequency band. After convolution optimization, the feature expression with time sequence relationship is obtained. The generic representation between the features and pseudo effective feature expression are iteratively learned in encoder E and generator G in the generative adversarial way. In addition, the continuous fusion for class intra tags can help to form continuous differences between classes. The experimental results show that the accuracy for the four classification is 81.32% and 54.87% respectively by using SAP and LOSO in DEAP database. Compared with other models, this algorithm can effectively improve the recognition performance.