3DC-BiL: a temporal enhanced model combining three-dimensional convolutional neural network and bidirectional long short-term memory networks for electroencephalography emotion recognition

Electroencephalography (EEG) signals are physiological signals that directly reflect the brain’s electrical activity, holding significant research value in the field of emotion recognition. However, existing methods still face certain limitations in jointly modeling temporal, spatial, and frequency-domain features. To address this, this article proposes an EEG-based emotion recognition model that integrates a three-dimensional convolutional neural network (3DCNN) with a bidirectional long short-term memory network (BiLSTM), referred to as 3DC-BiL. This approach utilizes 3DCNN to extract spatio-temporal features across different frequency bands, and then leverages BiLSTM to enhance temporal modeling capabilities, achieving collaborative fusion of multidimensional features. On the DEAP dataset for binary emotion classification, the proposed model achieves average accuracies of 98.60% and 98.66% on the arousal and valence dimensions, respectively. On the SEED dataset for three-class emotion classification, it achieves an average accuracy of 99.01%, outperforming various mainstream emotion recognition models in recent years. The experimental results demonstrate that the proposed model excels in EEG feature extraction and high-precision emotion classification, providing a new perspective and practical foundation for the research of EEG-based emotion recognition systems.