Abstract
Emotion recognition is a key technology of human-computer emotional interaction, which plays an important role in various fields and has attracted the attention of many researchers. However, the issue of interactivity and correlation between multi-channel EEG signals has not attracted much attention. For this reason, an EEG signal emotion recognition method based on 2DCNN-BiGRU and attention mechanism is tentatively proposed. This method firstly forms a two-dimensional matrix according to the electrode position, and then takes the pre-processed two-dimensional feature matrix as input, in the two-dimensional convolutional neural network (2DCNN) and the bidirectional gated recurrent unit (BiGRU) with the attention mechanism layer Extract spatial features and time domain features in, and finally classify by softmax function. The experimental results show that the average classification accuracy of this model are 93.66% and 94.32% in the valence and arousal, respectively.
Emotion recognition based on multi-channel EEG signals
