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Depression Detection from Three-Channel Resting-State EEG Using a Hybrid Conv1D and Spectral-Statistical Fusion

Oana-Isabela Știrbu1, Florin-Ciprian Argatu2, Felix-Constantin Adochiei2

  • 1Doctoral School of Electrical Engineering, Faculty of Electrical Engineering, National University of Science and Technology Politehnica Bucharest (NUSTPB), 060042 Bucharest, Romania.

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Summary
This summary is machine-generated.

This study shows a lightweight, three-channel electroencephalogram (EEG) model can effectively screen for major depressive disorder (MDD). The portable system achieves high accuracy, supporting its use in scalable, low-burden mental health assessments.

Keywords:
EEGdepression detectionfeature fusionhybrid deep learningmajor depressive disorderresting-statethree-channel EEG

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Area of Science:

  • Neuroscience
  • Medical Technology
  • Computational Psychiatry

Background:

  • Major depressive disorder (MDD) screening requires scalable, low-burden tools.
  • Resting-state electroencephalogram (EEG) offers potential for objective biomarkers.
  • Current EEG screening methods may lack portability and efficiency.

Purpose of the Study:

  • To evaluate a lightweight, three-channel resting-state EEG model for discriminating MDD from healthy controls.
  • To develop a portable and efficient screening tool for major depressive disorder.
  • To assess the model's feasibility on an independent cohort using portable hardware.

Main Methods:

  • A compact hybrid fusion model combining Conv1D embeddings with spectral-statistical descriptors was developed for three-channel EEG.
  • A subject-independent evaluation protocol with majority voting was implemented to prevent data leakage.
  • The model was trained on a public MDD dataset and tested on an independent portable EEG cohort without fine-tuning.

Main Results:

  • The hybrid model achieved 93.43% window-level accuracy on held-out subjects from the public dataset.
  • Preliminary external validation on a portable device showed promising feasibility, with subject-level performance reported with confidence intervals.
  • The model is compact (≈40.19 MB) and compatible with int8 quantization for resource-constrained hardware.

Conclusions:

  • A lightweight, three-channel EEG hybrid model demonstrates feasibility for major depressive disorder detection.
  • The findings support the development of portable, low-burden EEG screening tools for MDD.
  • Further clinical validation and on-device inference studies are warranted.