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Updated: May 28, 2026

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder
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Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

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

Hassan Ugail1, Newton Howard2, Ali Ahmed Elmahmudi1

  • 1Centre for Visual Computing and Intelligent Systems, University of Bradford, Bradford BD7 1DP, UK.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

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This summary is machine-generated.

This study shows that resting-state electroencephalography (EEG) can accurately distinguish major depressive disorder from healthy individuals. A rigorous subject-wise protocol using eight-channel EEG achieved high classification accuracy, supporting its use in objective depression screening.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Psychiatry

Background:

  • Objective diagnosis of major depressive disorder (MDD) is challenging, relying heavily on subjective clinical interviews and rating scales.
  • Resting-state electroencephalography (EEG) offers a non-invasive, low-cost, and wearable-compatible method for potential objective assessment.
  • Previous EEG studies faced limitations due to segment-level data leakage and inadequate handling of subject identity.

Purpose of the Study:

  • To evaluate the efficacy of a compact eight-channel resting-state EEG configuration for distinguishing MDD from healthy controls.
  • To implement a strictly leakage-free, subject-wise protocol to ensure reliable classification.
  • To address and correct subject-identity ambiguity in a public EEG dataset.

Main Methods:

Keywords:
Extra Treesbeta-band powerdata leakageelectroencephalographyfrontal alpha asymmetryinter-channel coherencemajor depressive disorderspectral featuressubject-wise evaluationwearable EEG

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  • Utilized a public EEG dataset, correcting subject-identity ambiguity to yield 56 valid participants.
  • Employed ten repeated subject-wise cross-validation splits for robust evaluation.
  • Compared five machine learning models, including Extra Trees and a multi-layer perceptron on spectral features, and three convolutional neural networks on raw EEG signals.

Main Results:

  • The Extra Trees classifier, using eight-channel spectral and asymmetry features, achieved a mean balanced accuracy of 93.5% (95% CI: 89.6%–96.8%).
  • The mean area under the receiver operating characteristic curve (AUC) reached 98.6% (95% CI: 96.2%–100.0%).
  • Ablation studies confirmed the utility of spectral features and highlighted the importance of inter-channel coherence, while demonstrating robustness against noisy dimensions.

Conclusions:

  • Compact, subject-wise evaluated EEG screening protocols show significant promise for objective depression assessment.
  • Rigorous control of data leakage and subject identity is crucial for reliable EEG-based diagnostic tools.
  • Eight-channel resting-state EEG, under strict protocols, can effectively differentiate individuals with MDD from healthy controls.