Epilepsy is the unstable state caused by excessive discharge of brain cells. In more than 30 percent of epilepsy cases, seizures cannot be controlled with medication or surgery. Refractory epilepsy seriously affects the health of patients and brings great economic burden to families. Therefore, this requires an effective seizure classification and prediction method to reduce risk in epilepsy patients. Researchers proposed machine learning or deep learning methods to predict seizures. However, automatic screening of electrode channels and improvement of predictive accuracy remain a challenge. A multi-channel feature fusion model CNN-Bi-LSTM.This method only requires simple preprocessing. CNN is responsible for extracting spatial features, Bi-LSTM is responsible for extracting temporal features, and finally, two channel weights are allocated through the attention mechanism to filter out the results of the more weighted electrode channel output classification. The performance of the model is tested on the CHB-MIT dataset, and the output is divided into three categories, including normal, pre-seizure and mid-seizure. The ten-fold cross-validation average accuracy is 94.83%, the precision is 94.84%, the recall is 94.84%, the F1-score is 94.83%, and the MCC is 92.26% across CHB-MIT EEG.The ten-fold cross-validation average accuracy of UCI data set is 77.62%, the precision is 77.66%, the recall is 77.62%, the F1-score is 77.60%, and the MCC is 72.03%. The results showed that this method is superior to existing methods and can predict the EEG signals of epilepsy in advance. This work will be extended to design a removable epilepsy predictive device for real-time use.

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.

In the field of neuroscience, epilepsy is a chronic non-communicable brain disease that affects approximately 50 million people worldwide. Electroencephalography (EEG) has become a key tool in detecting and characterizing human neurological diseases such as epilepsy. This rapid and accurate diagnosis allows doctors to provide timely and effective treatment to patients, significantly reducing the frequency of future seizures and the risk of related complications, which is crucial for ensuring the long-term health and quality of life of patients. Currently, deep learning technologies, particularly Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), have demonstrated significant improvements in accuracy across various fields. However, CNNs exhibit limitations in perceiving global dependencies, while LSTMs face challenges such as gradient vanishing in long sequences. This paper proposes a novel EEG recognition model, the Epileptic EEG Detection and Recognition Model based on Multiple Attention Mechanisms and Spatiotemporal Feature Fusion (MASF). MASF consists of a hybrid attention mechanism, Transformer encoder, and dot-product attention mechanism, which directly interprets the epileptic state from the raw EEG signals, thereby eliminating the need for extensive data preprocessing and feature extraction. It is worth noting that our method achieved an accuracy of 94.19% and 72.50% on the CHB-MIT and Bonn University datasets, respectively, in ten-fold cross-validation tests. In conclusion, the MASF method for epileptic seizure ictal detection based on EEG signals demonstrates significant potential in accelerating diagnosis and improving patient prognosis, especially since it achieves high accuracy without the need for extensive data preprocessing or feature extraction. The source code and dataset can be obtained from https://github.com/Xhuangzhentao/MASF-Model-.git.

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.

This study proposes an improved Residual Asymptotic Feature Pyramid Network (R-AFPN) to address challenges in small target detection from the Unmanned Aerial Vehicle (UAV) perspectives, such as scale imbalance, feature extraction difficulty, occlusion, and computational constraints. The R-AFPN integrates three key modules: Residual Asymptotic Feature Fusion (RAFF) for adaptive spatial fusion and cross-scale linking, Shallow Information Extraction (SIE) for capturing detailed shallow features, and Hierarchical Feature Fusion (HFF) for bottom-up incremental fusion to enhance deep feature details. Experimental results demonstrate that R-AFPN-L achieves 50.7% AP₅₀ on the TinyPerson dataset and 48.9% mAP₅₀ on the VisDrone2019 dataset, outperforming the baseline by 3% and 1.2%, respectively, while reducing parameters by 15.1%. This approach offers a lightweight, efficient solution for small target detection in UAV applications.