ConTL: Improving the Performance of EEG-based Emotion Recognition via the Incorporation of CNN, Transformer and LSTM

Hyunwook Kang, Byung Hyung Kim

http://doi.org/10.5626/JOK.2024.51.5.454

This paper proposes a hybrid-network called ConTL, which is composed of a convolutional neural network (CNN), Transformer, and long short-term memory (LSTM) for EEG-based emotion recognition. Firstly, CNN is exploited to learn local features from the input EEG signals. Then, the Transformer learns global temporal dependencies from the output features. To further learn sequential dependencies of the time domain, the output features from the Transformer are fed to the bi-directional LSTM. To verify the effects of the proposed model, we compared the classification accuracies with five state-of-the-art models. There was an 0.73% improvement on SEED-IV compared to CCNN, and improvements of 0.97% and 0.63% were observed compared to DGCNN for valence and arousal of DEAP, respectively.