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Related Experiment Video

Updated: May 9, 2026

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
07:12

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

MSFSNet: Multi-Source Few-Shot Adaptation Network for Cross-Subject Depression Recognition from EEG Signals.

Kang Wang, Yanan Zhang, Yingwei Zhang

    IEEE Journal of Biomedical and Health Informatics
    |May 7, 2026
    PubMed
    Summary
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    This study introduces a new method for recognizing depression using electroencephalography (EEG) brain signals. The approach significantly improves accuracy in identifying depression across different individuals, enhancing diagnostic reliability.

    Area of Science:

    • Neuroscience
    • Artificial Intelligence
    • Computational Psychiatry

    Background:

    • Depression is a widespread mental health issue with significant societal costs.
    • Current machine learning models for depression detection using EEG have limited generalizability across subjects.
    • This limitation hinders practical clinical application and early intervention strategies.

    Purpose of the Study:

    • To develop a novel cross-subject depression recognition method using electroencephalography (EEG).
    • To enhance model adaptability and reduce dependency on specific subject data.
    • To improve the accuracy and reliability of EEG-based depression detection.

    Main Methods:

    • Proposed a Multi-Source Few-Shot Adaptation (MSFSA) method integrating multi-source domain adaptation and ensemble learning.

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    Last Updated: May 9, 2026

    Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
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    Published on: August 2, 2021

    Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder
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  • Employed an alternating training mechanism combining unsupervised and few-shot adaptation to minimize subject-specific biases.
  • Utilized Alpha and low-Gamma band EEG features for classification.

    • Main Results:

    • Achieved a significant accuracy of 87.12% in cross-subject depression recognition on the MODMA EEG dataset.
    • Outperformed existing state-of-the-art methods, including HEMAsNet (80.67%) and WDANet (70.94%), under a 10-fold cross-subject validation protocol.
    • Demonstrated the effectiveness of Alpha and low-Gamma band features in improving classification performance.

    Conclusions:

    • The proposed MSFSA method effectively reduces subject dependency in EEG-based depression recognition.
    • This approach offers a promising solution for improving cross-subject adaptability in mental health diagnostics.
    • Enhanced EEG analysis holds potential for more accurate and accessible depression screening.