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Depression recognition from EEG based on nonlinear analysis and adaptive feature fusion.

Tao Wu1,2, Xia Liu1, Chenglong Zhang3

  • 1College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, China.

Computer Methods in Biomechanics and Biomedical Engineering
|February 13, 2026
PubMed
Summary

This study introduces a new framework using nonlinear analysis and adaptive feature fusion for electroencephalograph (EEG)-based depression recognition, significantly improving accuracy. Patients with depression show reduced brain activity complexity via EEG entropy.

Keywords:
Electroencephalograph nonlinear analysis adaptive fusion machine learningdepression recognitionentropy

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Area of Science:

  • Neuroscience
  • Affective Computing
  • Machine Learning

Background:

  • Machine learning shows promise for electroencephalograph (EEG)-based depression recognition.
  • Existing methods struggle to fuse heterogeneous features effectively, limiting performance.

Purpose of the Study:

  • To develop a novel affective computing framework for depression identification using EEG.
  • To enhance depression recognition by integrating nonlinear analysis and adaptive feature coupling.

Main Methods:

  • Utilized diverse entropy measures to analyze complex dynamics in EEG signals.
  • Implemented a weighted average fusion strategy for adaptive aggregation of multi-view features.
  • Mitigated redundant feature influence through the proposed fusion strategy.

Main Results:

  • The proposed framework significantly improved the accuracy of depression recognition on public datasets.
  • EEG entropy values were lower in patients with depression compared to healthy controls, indicating reduced brain activity complexity.
  • Visualization analysis supported the findings on altered brain complexity.

Conclusions:

  • Nonlinear analysis and adaptive feature fusion offer valuable insights for EEG-based depression recognition.
  • The framework demonstrates the potential for improved diagnostic accuracy in mental health through advanced signal processing.
  • Reduced EEG complexity in depression patients highlights potential biomarkers for the condition.