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Distinguishing Early Depression from Negative Emotion via Multi-Domain EEG Feature Fusion and Multi-Head Additive

Ruoyu Du1, Benbao Wang1, Haipeng Gao1

  • 1School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, No. 66, XinMofan Road, Gulou District, Nanjing 210003, China.

Entropy (Basel, Switzerland)
|February 27, 2026
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Summary

This study introduces a new AI model using EEG signals for objective depression screening. The framework accurately distinguishes depression from negative emotions, offering potential for real-time mental health monitoring.

Keywords:
EEGattention mechanismdepression recognitionfeature fusionnegative emotion

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Computational Psychiatry

Background:

  • Early depression diagnosis is challenged by subjective clinical assessments.
  • Objective screening methods are needed to improve diagnostic accuracy.
  • Electroencephalography (EEG) signals offer a potential biomarker for neurophysiological changes in depression.

Purpose of the Study:

  • To develop a lightweight neural network framework for objective depression screening using EEG signals.
  • To differentiate pathological depressive states from transient negative emotions.
  • To enhance the accuracy and efficiency of depression detection.

Main Methods:

  • Utilized Wavelet Packet Decomposition to create a multi-domain feature space from EEG signals.
  • Integrated frequency (α/β power spectral density ratio), spatial (normalized α-asymmetry), and non-linear (Sample Entropy) features.
  • Employed a multi-head additive attention mechanism to adaptively weigh diverse neurophysiological attributes.

Main Results:

  • Achieved 92.2% classification accuracy and a 93% F1-score on combined DEAP and HUSM datasets.
  • Demonstrated superior performance compared to Support Vector Machine (SVM) and standard deep learning models.
  • The proposed framework showed high computational efficiency and rapid convergence.

Conclusions:

  • The developed neural network framework provides an objective and effective method for depression screening using EEG.
  • The multi-domain feature integration and attention mechanism enhance discriminative power for detecting depressive states.
  • The model's efficiency suggests its viability for real-time clinical mental health monitoring.