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Depression detection using deep learning and large language models from multimodalities
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DOI：10.3389/fdgth.2026.1759857.png)
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Abstract
En 中文
Depression is a complex psychiatric disorder that affects neural functioning; cognition; emotion; and behavior; making objective assessment a persistent clinical challenge. Traditional diagnostic methods depend on subjective interpretation; whereas recent advances in deep learning have enabled automated; data-driven detection across physiological and behavioral modalities. Among unimodal approaches; electroencephalography (EEG) remains the most widely used due to its sensitivity to depression-related neurophysiological alterations. However; EEG models often rely on small; homogeneous datasets and controlled laboratory conditions; limiting their generalizability. Multimodal architectures that integrate speech; facial expression; and EEG features provide richer representations and consistently outperform single-modality systems. Transformer-based fusion mechanisms and attention-guided models effectively capture complementary cross-modal cues; achieving 90%–95% accuracy on controlled laboratory datasets such as SEED-IV; while yielding more conservative F1-scores of approximately 0.80–0.90 on ecologically valid community datasets such as DAIC-WOZ. The emergence of Large Language Models (LLMs) represents a further methodological shift; offering cross-modal alignment; contextual inference; and data-efficient adaptation through unified embedding spaces and few-shot capabilities. This mini-review synthesizes recent advances in EEG-based; multimodal; and LLM-driven depression detection. It evaluates how modality diversity and architectural sophistication enhance performance while critically examining persisting limitations in dataset diversity; standardization; interpretability; and clinical validation. The convergence of multimodal deep learning with LLM reasoning signals a promising direction toward scalable; explainable; and clinically deployable AI systems for the assessment of objective depression.
Keywords:
large language models
affective computing
deep learning architectures
EEG-based classification
multimodal depression detection
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