A multi-modal open dataset for mental-disorder analysis
Open accessAbstract
According to the WHO, the number of mental disorder patients, especially depression patients, has overgrown and become a leading contributor to the global burden of disease. With the rising of tools such as 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. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and recordings of spoken language data from clinically depressed patients and matching normal controls, who were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes data collected using a traditional 128-electrodes mounted elastic cap and a wearable 3-electrode EEG collector for pervasive computing applications. The 128-electrodes EEG signals of 53 participants were recorded as both in resting state and while doing the Dot probe tasks; the 3-electrode EEG signals of 55 participants were recorded in resting-state; the audio data of 52 participants were recorded during interviewing, reading, and picture description.
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The Multi-modal Open Dataset for Mental Disorder Analysis (MODMA) is a multi-modal dataset tailored for mental disorders, featuring both clinically depressed patients and individuals from the normal population.
“The Multi-modal Open Dataset for Mental Disorder Analysis (MODMA) [34] is a multi-modal dataset tailored for mental disorders, featuring both clinically depressed patients and individuals from the normal population.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [34]
MODMA comprises speech data and ECG data.
“It comprises speech data and ECG data.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [34]
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Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey
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Self-citationAMG: A Depression Detection Model with Autoencoder and Multi-Head Graph Convolutional Network
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Explainable multi-frequency and multi-region fusion model for affective brain-computer interfaces
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Self-citationDecoupled Multi-Perspective Fusion for Speech Depression Detection
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A Comprehensive Analysis of Speech Depression Recognition Systems
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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization
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Weakly-Supervised Depression Detection in Speech Through Self-Learning Based Label Correction
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A Multimodal Depression Consultation Dataset of Speech and Text with HAMD-17 Assessments
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Multimodal Depression Detection System Using Machine Learning
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Self-citationEEG-based Multimodal Emotion Recognition: Recent Progress, Challenges, and Future Directions
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Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond
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Self-citationACM-GNN: Adaptive Cluster-Oriented Modularity Graph Neural Network for EEG Depression Detection
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EEG-based Depression Identification using A Deep Learning Model
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Discovering the neuronal dynamics in major depressive disorder using Hidden Markov Model
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A graph-based method for automatic graded diagnosis of depression using EEG signals
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Diagnosis and Classification of Depressive Disorders using ML and DL Models
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A comprehensive study of audio profiling: Methods, applications, challenges, and future directions
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Auto-Masked Audio Spectrogram Transformer for depression detection from speech
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EEG-Based Depression Detection: A Temporal Domain Feature-Centric Machine Learning Approach
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Development of multimodal sentiment recognition and understanding
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An End-to-End framework for extracting observable cues of depression from diary recordings
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TDSFE-Net: A Temporal Dual-Stream Feature Extraction Network for Depression Detection From EEG
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Attention-based multi-scale convolution and conformer for EEG-based depression detection
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A survey on electronic health record driven multimodal representation learning
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Multimodal transformer for depression detection based on EEG and interview data
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- Authors
- Cai, H., Yuan, Z., Gao, Y., Sun, S., Li, N., Tian, F., Xiao, H., Li, J., Yang, Z., Li, X., Zhao, Q., Liu, Z., Yao, Z., Yang, M., Peng, H., Zhu, J., Zhang, X., Gao, G., Zheng, F., Li, R., Guo, Z., Ma, R., Yang, J., Zhang, L., Hu, X., Li, Y., & Hu, B.
- Affiliations
- Lanzhou University (CN)
- Lanzhou University Second Hospital (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Li, Yumin
- Hu, Bin
- ORCID
- Venue
- Scientific Data
- Published
- 2022-04-19
- Volume
- 9
- Issue
- 1
- Pages
- 178-178
- ISSN
- 2052-4463 (ISSN-L)
- PMC
- PMC9018722
- OpenAlex
- W4224247713
- Cited by
- 293
- References
- 41
- Citations by year
- 2026: 84 · 2025: 108 · 2024: 52 · 2023: 36 · 2022: 10 · 2021: 3
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Data (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lanzhou University Institutional Repository (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61632014, 61627808, 61210010, 62076113)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- mental illness, depression, EEG, physiological parameters, spoken language, audio recordings, multimodal dataset, wearable EEG, dot-probe task, resting state, diagnosis
- MeSH terms
- Artificial Intelligence, Mental Disorders, Electroencephalography, Humans
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