SZNeuroRSE‑8K: a large-scale multi-disorder resting-state EEG dataset with multi-domain neuropsychological assessment
Large-scale, high-density EEG datasets offer significant potential to uncover the neural mechanisms of disease and advance clinical applications, yet they remain scarce. Here, we present a dataset of 64-channel resting-state EEG recordings comprising 8,416 resting-state EEG recordings from 8,132 participants, encompassing a wide range of 15 neurological and neuropsychiatric disorder categories. Along with EEG recordings, the dataset includes demographic characteristics, diagnostic classifications, and neuropsychological assessment scores. All data underwent rigorous quality control and were fully de-identified in accordance with ethical and regulatory guidelines. This resource provides an unprecedented opportunity for subgroup and cross-disease comparisons, and the development of robust machine learning models for clinical EEG interpretation. Access to the dataset is managed under institutional agreements ensuring privacy and ethical compliance, supporting both fundamental research and translational applications in neurology and psychiatry.
Authors
- Gan Huang (ORCID: https://orcid.org/0000-0001-9895-6163)
- Zian Pei (ORCID: https://orcid.org/0000-0002-5530-6539)
- Zhuofeng Li (ORCID: https://orcid.org/0000-0002-5881-7553)
- Lin Zhu (ORCID: https://orcid.org/0000-0003-0336-8209)
- Zhenxi Song (ORCID: https://orcid.org/0000-0001-8574-0857)
- Chongyuan Lian (ORCID: https://orcid.org/0000-0003-3843-8596)
- Xiaoyong Lan
- Xuetao Shi (ORCID: https://orcid.org/0000-0001-9919-8931)
- Yi Guo (ORCID: https://orcid.org/0000-0002-9128-4779)
- Zhiguo Zhang (ORCID: https://orcid.org/0000-0003-3982-9900)
- Zeyuan Zhao
- Shaoxia Fan (ORCID: https://orcid.org/0009-0005-7239-543X)
- Ge Dang (ORCID: https://orcid.org/0000-0002-9843-8996)
- Xin Jiang (ORCID: https://orcid.org/0000-0003-4141-1538)
- Shuo Yang (ORCID: https://orcid.org/0000-0001-7931-4544)
- Yumei Liu (ORCID: https://orcid.org/0009-0002-0284-260X)
- Xiaolin Su (ORCID: https://orcid.org/0009-0003-9045-7257)
- Hua Huang
Institutions
- Jinan University (CN)
- Shenzhen University (CN)
- Harbin Institute of Technology (CN)
- Southern University of Science and Technology (CN)
- Key Laboratory of Guangdong Province (CN)
- Shenzhen Bay Laboratory (CN)
- Institute for Advanced Study (DE)
- Shenzhen Technology University (CN)
Publication Details
- Journal
- Brain-Apparatus Communication A Journal of Bacomics
- Published
- 2025-11-26
- DOI
- https://doi.org/10.1080/27706710.2025.2585594
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00