Sentiment recognition model of EEG signals combined with one-dimensional convolution and BiBASRU-AT

Abstract

Abstract To address the problems of low accuracy of EEG emotion sentiment and insufficient feature extraction ability of recurrent model, an EEG sentiment recognition model combining one-dimensional convolution and BiBASRU-AT is proposed. The data set is preprocessed in segments to expand the number of samples, and 62 channel local emotional features are extracted from one-dimensional convolution; The built-in self-attention simple recurrent unit is constructed to capture the multi-channel fusion features and the dependence between channels. The soft attention mechanism identifies the key features that have a great impact on the identification of emotional tendencies, and the linear layer outputs the positive, neutral and negative emotion recognition results. The experimental results on the EEG data set(SEED) show that the model achieves an average classification accuracy of 90.24%, which is higher than the excellent deep learning model compared with the experiment. The built-in self-attention simple recurrent unit has stronger feature capture ability, which proves the effectiveness of the model.