DSE-Mixer: A pure multilayer perceptron network for emotion recognition from EEG feature maps

Abstract

Background:Decoding emotions from brain maps is a challenging task. Convolutional Neural Network (CNN) is commonly used for EEG feature map. However, due to its local bias, CNN is unable to efficiently utilize the global spatial information of EEG signals which limits the accuracy of emotion recognition. New Methods:We design the Dual-scal EEG-Mixer(DSE-Mixer) model for EEG feature map processing. Its brain region mixer layer and electrode mixer layer are designed to fuse EEG information at different spatial scales. For each mixer layer, the structure of alternating mixing of rows and columns of the input table enables cross-regional and cross-Mchannel communication of EEG information. In addition, a channel attention mechanism is introduced to adaptively learn the importance of each channel. Results:On the DEAP dataset, the DSE-Mixer model achieved a binary classification accuracy of 95.19% for arousal and 95.22% for valence. For the four-class classification across valence and arousal, the accuracies were HVHA: 92.12%, HVLA: 89.77%, LVLA: 93.35%, and LVHA: 92.63%. On the SEED dataset, the average recognition accuracy for the three emotions (positive, negative, and neutral) is 93.69%. Comparison with existing methods:In the emotion recognition research based on the DEAP and SEED datasets, DSE-Mixer achieved a high ranking performance. Compared to the two commonly used model in computer vision field, CNN and Vision Transformer(VIT), DSE-Mixer achieved significantly higher classification accuracy while requiring much less computational complexity. Conclusions:DSE-Mixer provides a novel brain map processing model with a small size, demonstrating outstanding performance in emotion recognition.