EEG-ConvoBLSTM: A novel hybrid model for efficient EEG signal classification

Electroencephalogram (EEG) signals pose a challenge to emotion recognition (ER) tasks due to their complexity and individual differences. Conventional machine learning methods usually rely on handcrafted feature extraction and perform poorly in cross-subject ER. In recent years, deep learning methods have made significant progress in the analysis of EEG signals. However, existing methods still have limitations in the comprehensive modeling of temporal and spatial features and the capture of long-term dependent information. In this paper, we propose a new hybrid model to enhance the accuracy and cross-subject generalization of ER from EEG signals. In particular, the proposed model extracts local spatiotemporal features of EEG signals through convolutional layers. It further captures long-term sequential dependencies through a bidirectional long short-term memory network (BLSTM). The proposed model can achieve more comprehensive modeling of spatiotemporal features. The efficacy of the model was evaluated using the SJTU Emotion EEG Dataset (SEED), a widely used dataset for emotion recognition studies, with a comparison made with traditional machine learning methods and existing deep learning models. The experimental results demonstrate that the proposed hybrid model performs well in terms of accuracy, Kappa coefficient, and F1-score. The proposed model especially shows strong ability in distinguishing cross-subject emotion categories. In addition, ablation experiments verified the key role of the combination of convolution operation and BLSTM in improving model performance. The proposed model is useful for applications in multimodal data fusion and more complex ER tasks.