Leveraging deep learning for robust EEG analysis in mental health monitoring

Mental health monitoring utilizing EEG analysis has garnered notable interest due to the non-invasive characteristics and rich temporal information encoded in EEG signals, which are indicative of cognitive and emotional conditions. Conventional methods for EEG-based mental health evaluation often de...

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Vydáno v:Frontiers in neuroinformatics Ročník 18; s. 1494970
Hlavní autoři: Liu, Zixiang, Zhao, Juan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland Frontiers Research Foundation 03.01.2025
Frontiers Media S.A
Témata:
Adaptability
Algorithms
application of EEG
Brain research
Datasets
Deep learning
EEG
Electroencephalography
Graph representations
Information processing
Machine learning
Mental disorders
Mental health
mental health monitoring
neural electrical signals
Neural networks
Support vector machines
transformer
transformer
mental health monitoring
EEG
application of EEG
neural electrical signals
ISSN:1662-5196, 1662-5196
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Shrnutí:Mental health monitoring utilizing EEG analysis has garnered notable interest due to the non-invasive characteristics and rich temporal information encoded in EEG signals, which are indicative of cognitive and emotional conditions. Conventional methods for EEG-based mental health evaluation often depend on manually crafted features or basic machine learning approaches, like support vector classifiers or superficial neural networks. Despite the potential of these approaches, they often fall short in capturing the intricate spatiotemporal relationships within EEG data, leading to lower classification accuracy and poor adaptability across various populations and mental health scenarios. To overcome these limitations, we introduce the EEG Mind-Transformer, an innovative deep learning architecture composed of a Dynamic Temporal Graph Attention Mechanism (DT-GAM), a Hierarchical Graph Representation and Analysis (HGRA) module, and a Spatial-Temporal Fusion Module (STFM). The DT-GAM is designed to dynamically extract temporal dependencies within EEG data, while the HGRA models the brain's hierarchical structure to capture both localized and global interactions among different brain regions. The STFM synthesizes spatial and temporal elements, generating a comprehensive representation of EEG signals. Our empirical results confirm that the EEG Mind-Transformer significantly surpasses conventional approaches, achieving an accuracy of 92.5%, a recall of 91.3%, an F1-score of 90.8%, and an AUC of 94.2% across several datasets. These findings underline the model's robustness and its generalizability to diverse mental health conditions. Moreover, the EEG Mind-Transformer not only pushes the boundaries of state-of-the-art EEG-based mental health monitoring but also offers meaningful insights into the underlying brain functions associated with mental disorders, solidifying its value for both research and clinical settings.
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ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2024.1494970