Background: Emotions play a crucial role in people’s lives, profoundly affecting their cognition, decision-making, and interpersonal communication. Emotion recognition based on brain signals has become a significant challenge in the fields of affective computing and human-computer interaction. Methods: Addressing the issue of inaccurate feature extraction and low accuracy of existing deep learning models in emotion recognition, this paper proposes a multi-channel automatic classification model for emotion EEG signals named DACB, which is based on dual attention mechanisms, convolutional neural networks, and bidirectional long short-term memory networks. DACB extracts features in both temporal and spatial dimensions, incorporating not only convolutional neural networks but also SE attention mechanism modules for learning the importance of different channel features, thereby enhancing the network’s performance. DACB also introduces dot product attention mechanisms to learn the importance of spatial and temporal features, effectively improving the model’s accuracy. Results: The accuracy of this method in single-shot validation tests on the SEED-IV and DREAMER (Valence-Arousal-Dominance three-classification) datasets is 99.96% and 87.52%, 90.06%, and 89.05%, respectively. In 10-fold cross-validation tests, the accuracy is 99.73% and 84.26%, 85.40%, and 85.02%, outperforming other models. Conclusions: This demonstrates that the DACB model achieves high accuracy in emotion classification tasks, demonstrating outstanding performance and generalization ability and providing new directions for future research in EEG signal recognition.

Emotional states play a crucial role in shaping decision-making and social interactions, with sentiment analysis becoming an essential technology in human–computer emotional engagement, garnering increasing interest in artificial intelligence research. In EEG-based emotion analysis, the main challenges are feature extraction and classifier design, making the extraction of spatiotemporal information from EEG signals vital for effective emotion classification. Current methods largely depend on machine learning with manual feature extraction, while deep learning offers the advantage of automatic feature extraction and classification. Nonetheless, many deep learning approaches still necessitate manual preprocessing, which hampers accuracy and convenience. This paper introduces a novel deep learning technique that integrates multi-scale convolution and bidirectional long short-term memory networks with an attention mechanism for automatic EEG feature extraction and classification. By using raw EEG data, the method applies multi-scale convolutional neural networks and bidirectional long short-term memory networks to extract and merge features, selects key features via an attention mechanism, and classifies emotional EEG signals through a fully connected layer. The proposed model was evaluated on the SEED dataset for emotion classification. Experimental results demonstrate that this method effectively classifies EEG-based emotions, achieving classification accuracies of 99.44% for the three-class task and 99.85% for the four-class task in single validation, with average 10-fold-cross-validation accuracies of 99.49% and 99.70%, respectively. These findings suggest that the MSBiLSTM-Attention model is a powerful approach for emotion recognition.