Channel attention convolutional aggregation network based on video-level features for EEG emotion recognition

Electroencephalogram (EEG) emotion recognition plays a vital role in affective computing. A limitation of the EEG emotion recognition task is that the features of multiple domains are rarely included in the analysis simultaneously because of the lack of an effective feature organization form. This paper proposes a video-level feature organization method to effectively organize the temporal, frequency and spatial domain features. In addition, a deep neural network, Channel Attention Convolutional Aggregation Network, is designed to explore deeper emotional information from video-level features. The network uses a channel attention mechanism to adaptively captures critical EEG frequency bands. Then the frame-level representation of each time point is obtained by multi-layer convolution. Finally, the frame-level features are aggregated through NeXtVLAD to learn the time-sequence-related features. The method proposed in this paper achieves the best classification performance in SEED and DEAP datasets. The mean accuracy and standard deviation of the SEED dataset are 95.80% and 2.04%. In the DEAP dataset, the average accuracy with the standard deviation of arousal and valence are 98.97% ± 1.13% and 98.98% ± 0.98%, respectively. The experimental results show that our approach based on video-level features is effective for EEG emotion recognition tasks.