# MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

arXiv: https://arxiv.org/abs/2002.09283 · HTML: https://mlsanity.com/paper/2002.09283/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 30 of 38)

- [SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models](https://mlsanity.com/paper/2503.13503/) (2025)
- [DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI](https://mlsanity.com/paper/2601.06134/) (2026)
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- [Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges](https://mlsanity.com/paper/2407.16804/) (2024)
- [Machine Learning Fairness for Depression Detection using EEG Data](https://mlsanity.com/paper/2501.18192/) (2025)
- [The First MPDD Challenge: Multimodal Personality-aware Depression Detection](https://mlsanity.com/paper/2505.10034/) (2025)
- [Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey](https://mlsanity.com/paper/2403.08824/) (2024)
- [Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research](https://mlsanity.com/paper/2406.04116/) (2024)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [A Comprehensive Review of Datasets for Clinical Mental Health AI Systems](https://mlsanity.com/paper/2508.09809/) (2025)
- [SingLEM: Single-Channel Large EEG Model](https://mlsanity.com/paper/2509.17920/) (2025)
- [A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detection](https://mlsanity.com/paper/2410.18103/) (2024)
- [Fair Uncertainty Quantification for Depression Prediction](https://mlsanity.com/paper/2505.04931/) (2025)
- [Automatic Depression Assessment using Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/2506.18915/) (2025)
- [Hierarchical Self-Supervised Representation Learning for Depression Detection from Speech](https://mlsanity.com/paper/2510.08593/) (2025)
- [A spatiotemporal fused network considering electrode spatial topology and time-window transition for MDD detection](https://mlsanity.com/paper/2411.08521/) (2024)
- [Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarization](https://mlsanity.com/paper/2501.16106/) (2025)
- [FairSSL: Fair Multimodal Self-Supervised Learning](https://mlsanity.com/paper/2508.16748/) (2025)
- [A Multi-Probe Audit of Clinical-Interview Depression Detection Benchmarks](https://mlsanity.com/paper/2605.23977/) (2026)
- [EEG Based Generative Depression Discriminator](https://mlsanity.com/paper/2402.09421/) (2024)
- [Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method](https://mlsanity.com/paper/2503.13475/) (2025)
- [KnowEEG: Explainable Knowledge Driven EEG Classification](https://mlsanity.com/paper/2505.00541/) (2025)
- [RBA-FE: A Robust Brain-Inspired Audio Feature Extractor for Depression Diagnosis](https://mlsanity.com/paper/2506.07118/) (2025)
- [Class Unbiasing for Generalization in Medical Diagnosis](https://mlsanity.com/paper/2508.06943/) (2025)
- [AI Models for Depressive Disorder Detection and Diagnosis: A Review](https://mlsanity.com/paper/2508.12022/) (2025)
- [Exploring Machine Learning and Language Models for Multimodal Depression Detection](https://mlsanity.com/paper/2508.20805/) (2025)
- [E-THER: A Multimodal Dataset for Empathic AI -- Towards Emotional Mismatch Awareness](https://mlsanity.com/paper/2509.02100/) (2025)
- [TRI-DEP: A Trimodal Comparative Study for Depression Detection Using Speech, Text, and EEG](https://mlsanity.com/paper/2510.14922/) (2025)
- [Incomplete Depression Feature Selection with Missing EEG Channels](https://mlsanity.com/paper/2511.11651/) (2025)
- [MF-GCN: A Multi-Frequency Graph Convolutional Network for Tri-Modal Depression Detection Using Eye-Tracking, Facial, and Acoustic Features](https://mlsanity.com/paper/2511.15675/) (2025)

## Abstract (arXiv)

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis.