EEG Emotion Recognition Based on Frequency and Channel Convolutional Attention

Abstract

The existing emotion recognition researches generally use neural network and attention mechanism to learn emotional features,which have relatively single feature representation.Moreover,neuroscience studies have shown that EEG signals of different frequencies and channels have different responses to emotion.Therefore,this paper proposes a method of fusing frequency and electrode channel convolutional attention for EEG emotion recognition.Specifically,EEG signals are firstly decomposed into different frequency bands and the corresponding frame-level features are extracted.Then the pre-activated residual network is employed to learn deep emotion-relevant features.At the same time,the frequency and electrode channel convolutional attention module is integrated into each pre-activated residual unit of residual network to model the frequency and channel information of EEG signals,thus generating final representation of EEG features.Experiments on DEAP and DREAMER datasets show that the proposed method helps to enhance the importing of emotion-salient information in EEG signals when compared with single-layer attention mechanism,and generates better recognition performance.

Keywords

• eeg emotion recognition|feature representation|residual network|pre-activated residual unit|frequency and electrode channel convolutional attention