Leveraging deep learning for robust EEG analysis in mental health monitoring
IntroductionMental 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 evaluat...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2024.1494970/full |
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| author | Zixiang Liu Juan Zhao |
| author_facet | Zixiang Liu Juan Zhao |
| author_sort | Zixiang Liu |
| collection | DOAJ |
| description | IntroductionMental 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.MethodsTo 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.Results and discussionOur 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. |
| format | Article |
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| institution | Kabale University |
| issn | 1662-5196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroinformatics |
| spelling | doaj-art-be50c8b27f7d4af0b5f3a3e98f0f2ffb2025-01-03T06:47:20ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962025-01-011810.3389/fninf.2024.14949701494970Leveraging deep learning for robust EEG analysis in mental health monitoringZixiang Liu0Juan Zhao1Anhui Vocational College of Grain Engineering, Hefei, ChinaHefei University, Hefei, ChinaIntroductionMental 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.MethodsTo 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.Results and discussionOur 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.https://www.frontiersin.org/articles/10.3389/fninf.2024.1494970/fullEEGmental health monitoringtransformerapplication of EEGneural electrical signals |
| spellingShingle | Zixiang Liu Juan Zhao Leveraging deep learning for robust EEG analysis in mental health monitoring Frontiers in Neuroinformatics EEG mental health monitoring transformer application of EEG neural electrical signals |
| title | Leveraging deep learning for robust EEG analysis in mental health monitoring |
| title_full | Leveraging deep learning for robust EEG analysis in mental health monitoring |
| title_fullStr | Leveraging deep learning for robust EEG analysis in mental health monitoring |
| title_full_unstemmed | Leveraging deep learning for robust EEG analysis in mental health monitoring |
| title_short | Leveraging deep learning for robust EEG analysis in mental health monitoring |
| title_sort | leveraging deep learning for robust eeg analysis in mental health monitoring |
| topic | EEG mental health monitoring transformer application of EEG neural electrical signals |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2024.1494970/full |
| work_keys_str_mv | AT zixiangliu leveragingdeeplearningforrobusteeganalysisinmentalhealthmonitoring AT juanzhao leveragingdeeplearningforrobusteeganalysisinmentalhealthmonitoring |