Depression is a widespread mental health disorder, yet its automatic detection remains challenging. Prior work has explored unimodal and multimodal approaches, with multimodal systems showing promise by leveraging complementary signals. However, existing studies are limited in scope, lack systematic comparisons of features, and suffer from inconsistent evaluation protocols. We address these gaps by systematically exploring feature representations and modelling strategies across EEG, together with speech and text. We evaluate handcrafted features versus pre-trained embeddings, assess the effectiveness of different neural encoders, compare unimodal, bimodal, and trimodal configurations, and analyse fusion strategies with attention to the role of EEG. Consistent subject-independent splits are applied to ensure robust, reproducible benchmarking. Our results show that (i) the combination of EEG, speech and text modalities enhances multimodal detection, (ii) pretrained embeddings outperform handcrafted features, and (iii) carefully designed trimodal models achieve state-of-the-art performance. Our work lays the groundwork for future research in multimodal depression detection.

翻译：抑郁症是一种普遍存在的心理健康障碍，但其自动检测仍具挑战性。先前的研究已探索了单模态与多模态方法，其中多模态系统通过利用互补信号展现出潜力。然而，现有研究范围有限，缺乏对特征的系统性比较，且评估协议不一致。我们通过系统探索脑电信号（EEG）与语音、文本的特征表示和建模策略，以弥补这些不足。我们评估了手工特征与预训练嵌入的性能，检验了不同神经编码器的有效性，比较了单模态、双模态和三模态配置，并分析了融合策略，特别关注脑电信号的作用。研究采用一致的受试者独立划分以确保稳健、可复现的基准测试。结果表明：（i）脑电、语音和文本模态的结合能增强多模态检测性能；（ii）预训练嵌入优于手工特征；（iii）精心设计的三模态模型达到了最先进的性能。本研究为未来多模态抑郁症检测研究奠定了基础。