EEG Emotion Recognition based on Multi scale Self Attention Convolutional Networks




Multi-Channel EEG Signal, Emotional Recognition, Multi-Scale Convolutional Network, Self-Attention Network


A multi-view self-attention module is proposed and paired with a multi-scale convolutional model to build
a multi-view self-attention convolutional network for multi-channel EEG emotion recognition. First, time
and frequency domain characteristics are extracted from multi-channel EEG signals, and a three-dimensional
feature matrix is built using spatial mapping connections. Then, a multi-scale convolutional network extracts
the high-level abstract features from the feature matrix, and a multi-view self-attention network strengthens
the features. Finally, use the multilayer perceptron for sentiment classification. The experimental results reveal
that the multi-view self-attention convolutional network can effectively integrate the time domain, frequency
domain, and spatial domain elements of EEG signals using the DEAP public emotion dataset. The multi-view
self-attention module can eliminate superfluous data, apply attention weight to the network to hasten network
convergence, and enhance model recognition precision.


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How to Cite

H. Chao and F. Yuan, “EEG Emotion Recognition based on Multi scale Self Attention Convolutional Networks ”, EAI Endorsed Trans e-Learn, vol. 8, no. 4, p. e4, Sep. 2023.