Cited by 293Emotion and Mood RecognitionEEG and Brain-Computer InterfacesMental Health via Writing

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A multi-modal open dataset for mental-disorder analysis

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Cai, Hanshu, Yuan, Zhenqin, Gao, Yiwen, Sun, Shuting, Li, Na, Tian, Fuze, Xiao, Han, Li, Jianxiu, Yang, Zhengwu, Li, Xiaowei, Zhao, Qinglin, Liu, Zhenyu, Yao, Zhijun, Yang, Minqiang, Peng, Hong, Zhu, Jing, Zhang, Xiaowei, Gao, Guoping, Zheng, Fang, Li, Rui, Guo, Zhihua, Ma, Rong, Yang, Jing, Zhang, Lan, Hu, Xiping, Li, Yumin, Hu, Bin·2022·Scientific Data

DOI 10.1038/s41597-022-01211-xPMC PMC9018722

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Abstract

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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Fig. 1 Three-electrode pervasive EEG collection device.CC BY · PubMed Central

Fig. 2 The trial sequence of the dot-probe task. The cue stimuli include three kinds of emotional-neutral face pairs (Fear-Neutral, Sad-Neutral, and Happy-Neutral). The dot target was presented randomly as a target in either the left or right position of the fixed cross.CC BY · PubMed Central

Fig. 3 Location of the three electrodes placement and the three-electrode pervasive EEG collection device.CC BY · PubMed Central

Fig. 4 The real-time visual display system shows the EEG signals after processing in real-time.CC BY · PubMed Central

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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: Cai, Hanshu
Gao, Yiwen
Sun, Shuting
Li, Na
Tian, Fuze
Li, Jianxiu
Li, Xiaowei
Zhao, Qinglin
Liu, Zhenyu
Yao, Zhijun
Yang, Minqiang
Peng, Hong
Zhu, Jing
Zhang, Xiaowei
Li, Rui
Guo, Zhihua
Ma, Rong
Yang, Jing
Zhang, Lan
Hu, Xiping
Li, Yumin
Hu, Bin
Venue: Scientific Data
Publisher: Springer Science and Business Media LLC
Published: 2022-04-19
Volume: 9
Issue: 1
Pages: 178-178
ISSN: 2052-4463 (ISSN-L)
DOI: 10.1038/s41597-022-01211-x
PMC: PMC9018722
OpenAlex: W4224247713
Cited by: 293
References: 41
Citations by year: 2026: 84 · 2025: 108 · 2024: 52 · 2023: 36 · 2022: 10
Type: data paper
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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: Physical Sciences › Computer Science › Artificial Intelligence › 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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A multi-modal open dataset for mental-disorder analysis

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