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Investigating Emotion EEG Patterns for Depression Detection with Attentive Simple Graph Convolutional Network.

Yu-Ting Lan, Dan Peng, Wei Liu

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    This study shows that emotion-based electroencephalography (EEG) patterns can help detect depression. Using dry EEG electrodes and a novel AI model, researchers identified distinct brain activity in depressed individuals, aiding in diagnosis.

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    Area of Science:

    • Neuroscience
    • Psychiatry
    • Biomedical Engineering

    Background:

    • Depression significantly impairs daily functioning and quality of life, with potential for self-harm.
    • Noninvasive electroencephalography (EEG) shows promise as an objective biomarker for depression diagnosis and treatment monitoring.
    • Dry EEG electrodes enhance the clinical accessibility of EEG-based biomarkers.

    Purpose of the Study:

    • To systematically investigate the potential of emotion-induced EEG patterns for depression detection using a dry EEG electrode system.
    • To explore differences in brain activity between depressed patients and healthy controls during emotional stimuli.
    • To develop and evaluate a deep learning model for classifying depression based on EEG data.

    Main Methods:

    • Collected EEG signals from 33 depressed patients and 40 healthy controls during an emotion elicitation paradigm (happy, neutral, sad emotions) using film stimuli.
    • Analyzed mean activation levels in alpha, beta, and gamma bands at frontal and temporal sites.
    • Developed an Attentive Simple Graph Convolutional network to incorporate EEG channel topology for emotion recognition and depression detection.

    Main Results:

    • Significant differences in mean activation levels were observed between depressed and healthy groups across various frequency bands and electrode sites.
    • The developed deep learning classifier achieved high performance, with a sensitivity of 81.93% and a specificity of 91.69% for distinguishing depressed patients from controls using happy emotion data.

    Conclusions:

    • Emotion-induced EEG patterns, captured by dry electrodes, show potential as reliable biomarkers for objective depression detection.
    • The findings suggest that depressive symptoms alter emotional experiences and associated neural activity.
    • The Attentive Simple Graph Convolutional network effectively leverages EEG topology for enhanced depression classification.