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Explainable Temporal Deep Learning for EEG‐Based Depression Detection Using Resting‐State Brain Dynamics.

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Title: Explainable Temporal Deep Learning for EEG‐Based Depression Detection Using Resting‐State Brain Dynamics.
Authors: Naeim, Mahdi (AUTHOR), Atadokht, Akbar (AUTHOR)
Source: International Journal of Methods in Psychiatric Research. Jun2026, Vol. 35 Issue 2, p1-13. 13p.
Subjects: Electroencephalography, Mental depression, Dynamic models, Computational neuroscience, Artificial intelligence, Deep learning
Abstract: Background: Depression is a major mental health disorder, and EEG‐based automated detection is emerging as a potential objective diagnostic tool. However, achieving both high accuracy and interpretability remains challenging due to the complex spatiotemporal structure of EEG signals. This study proposes an explainable deep learning framework for depression detection using resting‐state EEG data. Methods: A retrospective computational study using deep learning was conducted using EEG data from 122 subjects in the OpenNeuro dataset (ds003478). Based on Beck Depression Inventory (BDI) scores, participants were classified into control (BDI ≤ 13; n = 76) and depressive (BDI ≥ 20; n = 30) groups; intermediate cases (BDI 14–19; n = 16) were excluded to reduce label ambiguity and construct a high‐confidence binary classification framework, although this may reduce applicability to mild or subclinical depression, resulting in a final cohort of 106 subjects. Preprocessing included band‐pass filtering (1–40 Hz), 50 Hz notch filtering, Independent Component Analysis, and average referencing. A subject‐wise 5‐fold cross‐validation was applied. A CNN–BiLSTM architecture with an attention mechanism integrated with explainable AI techniques (Grad‐CAM and SHAP) was developed. Results: The proposed model achieved an accuracy of 89.76%, an F1‐score of 89.58%, and an AUC of 0.936. Ablation analysis confirmed the contribution of temporal modeling and attention mechanisms. Explainability analysis using Grad‐CAM and SHAP showed that frontal EEG channels were the most influential in classification, consistent with neurophysiological findings. Conclusion: The proposed framework provides an accurate and interpretable method for EEG‐based depression detection, supporting applications in computational psychiatry and decision‐support systems. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Methods in Psychiatric Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Explainable Temporal Deep Learning for EEG‐Based Depression Detection Using Resting‐State Brain Dynamics.
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  Data: <searchLink fieldCode="AR" term="%22Naeim%2C+Mahdi%22">Naeim, Mahdi</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Atadokht%2C+Akbar%22">Atadokht, Akbar</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Methods+in+Psychiatric+Research%22">International Journal of Methods in Psychiatric Research</searchLink>. Jun2026, Vol. 35 Issue 2, p1-13. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+depression%22">Mental depression</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+models%22">Dynamic models</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+neuroscience%22">Computational neuroscience</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
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  Data: Background: Depression is a major mental health disorder, and EEG‐based automated detection is emerging as a potential objective diagnostic tool. However, achieving both high accuracy and interpretability remains challenging due to the complex spatiotemporal structure of EEG signals. This study proposes an explainable deep learning framework for depression detection using resting‐state EEG data. Methods: A retrospective computational study using deep learning was conducted using EEG data from 122 subjects in the OpenNeuro dataset (ds003478). Based on Beck Depression Inventory (BDI) scores, participants were classified into control (BDI ≤ 13; n = 76) and depressive (BDI ≥ 20; n = 30) groups; intermediate cases (BDI 14–19; n = 16) were excluded to reduce label ambiguity and construct a high‐confidence binary classification framework, although this may reduce applicability to mild or subclinical depression, resulting in a final cohort of 106 subjects. Preprocessing included band‐pass filtering (1–40 Hz), 50 Hz notch filtering, Independent Component Analysis, and average referencing. A subject‐wise 5‐fold cross‐validation was applied. A CNN–BiLSTM architecture with an attention mechanism integrated with explainable AI techniques (Grad‐CAM and SHAP) was developed. Results: The proposed model achieved an accuracy of 89.76%, an F1‐score of 89.58%, and an AUC of 0.936. Ablation analysis confirmed the contribution of temporal modeling and attention mechanisms. Explainability analysis using Grad‐CAM and SHAP showed that frontal EEG channels were the most influential in classification, consistent with neurophysiological findings. Conclusion: The proposed framework provides an accurate and interpretable method for EEG‐based depression detection, supporting applications in computational psychiatry and decision‐support systems. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Methods in Psychiatric Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/mpr.70088
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        Text: English
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      – SubjectFull: Dynamic models
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              Text: Jun2026
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