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

Overview

1. Department of Psychology, Faculty of Educational Sciences and Psychology, University of Mohaghegh Ardabili, Ardabil, Iran

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

• doi:10.18112/openneuro.ds003478.v1.1.0 — at OpenNeuro; found in the acknowledgements
• openneuro:ds003478 — at OpenNeuro; found in “Data Availability Statement”

Data Availability Statement

The data that support the findings of this study are openly available in OpenNeuro at https://openneuro.org/datasets/ds003478/versions/1.1.0, reference number ds003478. This dataset, entitled EEG: Depression Rest, contains resting‐state EEG recordings from 122 participants. The data were collected at the University of Arizona, USA, in compliance with ethical approval (Ref: 054/13‐CER‐FR) and are shared in the BIDS format. The analyses presented in this manuscript were conducted using this publicly available dataset.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 5 keywords, 9 MeSH terms, 30 references.

Cite

This paper

Naeim, M., & Atadokht, A. (2026). Explainable Temporal Deep Learning for EEG-Based Depression Detection Using Resting-State Brain Dynamics. International journal of methods in psychiatric research, 35(2), e70088. https://doi.org/10.1002/mpr.70088

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1002/cns.70943

Stability-Driven Selection of EEG Connectivity Features for Psychosis Classification: A Network-Based Machine Learning Approach.

Journal: CNS neuroscience & therapeutics

In common: EEG, author Mahdi Naeim

[2] doi:10.3390/s26103065 [code]

Subject-Wise Depression Screening from Eight-Channel Resting-State EEG Using Asymmetry-Aware Spectral Features and Connectivity Ablation.

Journal: Sensors (Basel, Switzerland)

In common: depression, EEG, 2 references

[3] doi:10.1186/s40708-026-00320-2

Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection.

Journal: Brain informatics

In common: EEG, 2 references

[4] doi:10.1371/journal.pone.0357213

Integrating socioeconomic context with multimodal EEG data for improved ADHD risk screening.

Journal: PloS one

In common: EEG, 2 references

[5] doi:10.1038/s41598-026-56070-y [code]

SSDLabeler: realistic semi-synthetic data generation for multi-label artifact classification in EEG.

Journal: Scientific reports

In common: EEG, 2 references

[6] doi:10.3390/diagnostics16132012

Power and Phase Fusion Spectrogram with Three-Dimensional Convolution and Vision Transformer for Seizure Detection.

Journal: Diagnostics (Basel, Switzerland)

In common: EEG, 2 references

[7] doi:10.3390/biomimetics11060377

PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.

Journal: Biomimetics (Basel, Switzerland)

In common: EEG, 2 references

[8] doi:10.1002/hbm.70628 [code]

EEG Biomarkers for Affective Disorders Diagnosis: An Evaluation and Validation Study.

Journal: Human brain mapping

In common: depression, EEG, 1 reference

[9] doi:10.1007/s00221-026-07368-w

The role of catastrophizing in anxiety and depression symptom scores among knee osteoarthritis patients: a multimodal assessment with EEG and machine learning.

Journal: Experimental brain research

In common: depression, EEG, 1 reference

[10] doi:10.1038/s41598-026-47945-1 [code]

GACNet: A graph attention-based neural network for electroencephalography localization via contrastive learning.

Journal: Scientific reports

In common: EEG, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.