What question did this study set out to answer?

To assess different subtypes of depression through emotion-modulated brain activity detected by EEG.

March 26, 2026Open Access

Differentiating Depression Subtypes through three-electrode EEG and Emotional Stimuli: A Machine Learning Approach

Key Points

To assess different subtypes of depression through emotion-modulated brain activity detected by EEG.
EEG recorded from 70 participants during immersive VR with emotional stimuli.
Analysis focused on the immediate post-stimulus period and group-by-condition interactions.
Developed a Bi-Emotional Siamese Network (BESN) to model EEG data for classification.
BESN achieved 83.1% accuracy in discriminating healthy controls from first depressive episodes.
Achieved 70.6% accuracy for three-class classification of healthy controls, first depressive episodes, and recurrent depressive episodes.
Results were validated on an external EEG dataset, achieving 78.2% accuracy.

Abstract

We propose an EEG-based framework for depression subtype assessment using emotion-modulated neural dynamics elicited by immersive virtual reality (VR). EEG was recorded from 70 participants (31 first depressive episode, FDE; 18 recurrent depressive episode, RDE; 21 control participants, HC) using a compact frontal montage (Fp1/Fpz/Fp2) during positive and negative VR conditions, focusing on the immediate post-stimulus regulation period. Subject-level features were analyzed with a linear mixed-effects model to probe group-by-condition interaction patterns, and a Bi-Emotional Siamese Network (BESN) was developed to model baseline-anchored within-subject deviations by fusing positive/negative reactivity streams with path-signature-based temporal encoding. Feature-level analyses revealed interaction signals suggestive of differential emotion-related modulation across groups. In subject-wise evaluation, BESN achieved 83.1% accuracy for HC vs FDE discrimination and 70.6% accuracy for three-class classification (HC vs FDE vs RDE), outperforming conventional machine-learning baselines. Robustness was further supported on an external public resting-state EEG dataset (MODMA), achieving 78.2% accuracy. These results suggest that baseline-anchored, emotion-modulated EEG dynamics combined with path-signature modeling provide an objective and generalizable computational approach for depression-related group discrimination.

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