Research on the Progress of Deep Learning in Emotion Classification of Electroencephalogram Signals
DOI:
https://doi.org/10.53469/wjimt.2025.08(05).12Keywords:
Deep Learning, EEG, Emotion Classification, Convolutional Neural Networks, Recurrent Neural NetworksAbstract
Emotion classification of electroencephalogram (EEG) signals is currently a research hotspot in the intersection of brain science and artificial intelligence, which is of great significance for revealing the mechanism of human emotions and achieving human-computer emotional interaction. In recent years, deep learning technology has made remarkable progress in the emotion classification of electroencephalogram (EEG) signals. This paper reviews the current research status of deep learning in the emotion classification of electroencephalogram (EEG) signals, analyzes the application characteristics and advantages of common deep learning models in this field, discusses key technologies such as data preprocessing, feature extraction, and model optimization, and looks forward to future research directions, aiming to provide a reference for the further development of this field.
References
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018). EEGNet: A compact convolutional neural network for EEG-based brain–computer interfaces. Journal of Neural Engineering, 15(5), 056013.
Li, X., Song, D., Zhang, P., & Yao, L. (2018). Exploring EEG features in cross-subject emotion recognition. Frontiers in Neuroscience, 12, 162.
Lotte, F., Bougrain, L., & Cichocki, A. (2018). A review of classification algorithms for EEG-based brain-computer interfaces: A 10 year update. Journal of Neural Engineering, 15(3), 031005.
Song, T., Wen, W., Liao, W., & Zheng, W. L. (2018). EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Transactions on Affective Computing, 11(3), 532-541.
Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., ... & Ball, T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping, 38(11), 5391-5420.
Zheng, W. L., & Lu, B. L. (2015). Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Transactions on Autonomous Mental Development, 7(3), 162-175.
Chen, Z., Liu, Y., & Li, H. (2020). EEG-based emotion recognition using regional-asymmetric spatiotemporal pattern recurrent neural networks. IEEE Transactions on Affective Computing, 11(3), 524-536.
Craik, A., He, Y., & Contreras-Vidal, J. L. (2019). Deep learning for electroencephalogram (EEG) classification tasks: A review. Journal of Neural Engineering, 16(2), 026030.
Wang, Y., Zheng, W. L., Yu, H. J., & Lu, B. L. (2019). Emotion recognition using multimodal residual LSTM networks. IEEE Transactions on Affective Computing, 12(2), 271-283.