A model for epileptic EEG detection and recognition based on Multi-Attention mechanism and Spatiotemporal

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 .