In the field of clinical neurology, automated detection of epileptic seizures based on electroencephalogram (EEG) signals has the potential to significantly accelerate the diagnosis of epilepsy. This rapid and accurate diagnosis enables doctors to provide timely and effective treatment for patients, significantly reducing the frequency of future epileptic seizures and the risk of related complications, which is crucial for safeguarding patients’ long-term health and quality of life. Presently, deep learning techniques, particularly Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs), have demonstrated remarkable accuracy improvements across various domains. Consequently, researchers have utilized these methodologies in studies focused on recognizing epileptic signals through EEG analysis. However, current models based on CNN and LSTM still heavily rely on data preprocessing and feature extraction steps. Additionally, CNNs exhibit limitations in perceiving global dependencies, while LSTMs encounter challenges such as gradient vanishing in long sequences. This paper introduced an innovative EEG recognition model, that is the Spatio-temporal feature fusion epilepsy EEG recognition model with dual attention mechanism (STFFDA). STFFDA is comprised of a multi-channel framework that directly interprets epileptic states from raw EEG signals, thereby eliminating the need for extensive data preprocessing and feature extraction. Notably, our method demonstrates impressive accuracy results, achieving 95.18% and 77.65% on single-validation tests conducted on the datasets of CHB-MIT and Bonn University, respectively. Additionally, in the 10-fold cross-validation tests, their accuracy rates were 92.42% and 67.24%, respectively. In summary, it is demonstrated that the seizure detection method STFFD based on EEG signals has significant potential in accelerating diagnosis and improving patient prognosis, especially since it can achieve high accuracy rates without extensive data preprocessing or feature extraction.

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.