CBAM-LSTM-attention enabled human emotion recognition using EEG signals

Abstract

Human emotion recognition seeks to facilitate machines in comprehending human emotional states. EEG signals, being non-invasive measurements, are among the signals that most accurately represent human emotions. Nevertheless, the abundance of channels in EEG signals leads to heightened computational complexity and a greater risk of overfitting the model. In this research, eXtreme Gradient Boosting (XGBoost) was employed to choose the suitable EEG channel, and a unique model named convolutional block attention module − long short-term memory − attention module (CBAM-LSTM-Attention) was introduced for emotion recognition. The model combines residual blocks, LSTM networks, and attention mechanisms to efficiently incorporate important channel-spatial and temporal domain features of EEG signals. To achieve high prediction accuracy in emotion recognition, the following innovations can be implemented: (i) Integrating XGBoost algorithm to evaluate each channel based on power spectral density (PSD), and selecting the channel with the highest score as the input for the model. This approach reduces the computational complexity of the model and minimises the risk of overfitting. (ii) Introducing a channel-spatial attention module in the residual block to enhance the model’s ability to extract channel-spatial domain features in the convolutional block attention module (CBAM) model. (iii) Utilising a multi-head attention mechanism to improve the model’s capability to extract temporal domain features, enabling global feature perception and input to the LSTM layer for decoding. The results indicated that the proposed CBAM-LSTM-Attention model achieved 95.108 % accuracy for arousal and 94.862 % for valence on the DEAP dataset using single-channel data. Using multi-channel data, the model achieved 98.790 % for arousal and 97.249 %for valence. This suggests that the model effectively enables correct classification of human emotion recognition.