TRI-DEP: A Trimodal Comparative Study for Depression Detection Using Speech, Text, and EEG
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a significant advancement in multimodal depression detection by systematically comparing various feature representations and modeling strategies across EEG, speech, and text modalities. The comprehensive methodology and rigorous experimental evaluation contribute to its potential impact on the field of machine learning and mental health diagnostics.
Methodology
The methodology is robust, employing a systematic approach to compare unimodal, bimodal, and trimodal configurations for depression detection. The authors effectively utilize both handcrafted features and pre-trained embeddings across EEG, speech, and text modalities. The use of consistent subject-independent splits enhances the reliability of the results, and the detailed description of the experimental pipeline demonstrates a thorough understanding of the complexities involved in multimodal data processing. The integration of various neural architectures and fusion strategies is well-justified, contributing to the overall strength of the proposed framework.
Results & evaluation
The experiments are comprehensive, utilizing a well-defined dataset (MODMA) and employing stratified 5-fold cross-validation to prevent data leakage. The results are clearly presented, showing significant improvements in performance when using multimodal approaches compared to unimodal ones. The authors provide detailed performance metrics, including F1-scores, which facilitate a clear understanding of the effectiveness of different configurations. However, the paper could benefit from more extensive comparisons with existing state-of-the-art methods to contextualize its contributions.
Reproducibility
The authors emphasize reproducibility by providing consistent data splits and committing to releasing their code and model checkpoints. This is crucial for fostering transparency in research, especially in a field where replicability is often a concern. However, the actual URLs for the code and data are not provided, which limits immediate access for other researchers.
Limitations
While the study addresses several gaps in the literature, it is limited by the relatively small sample size (38 subjects) and the reliance on automatic transcription for text data, which may introduce inaccuracies. Additionally, the study does not explore the potential impact of demographic variables on model performance, which could be significant in a clinical context.
Broader impact
The findings have significant implications for mental health diagnostics, particularly in enhancing the accuracy of depression detection through multimodal approaches. The work lays a foundation for future research in this area, potentially leading to more effective telemedicine applications and supporting clinical decision-making. The systematic exploration of feature representations and fusion strategies could inspire further innovations in multimodal machine learning applications beyond mental health. This paper presents a significant advancement in multimodal depression detection by systematically comparing various feature representations and modeling strategies across EEG, speech, and text modalities. The comprehensive methodology and rigorous experimental evaluation contribute to its potential impact on the field of machine learning and mental health diagnostics.
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