Our research is focused on resting-state EEG and simultaneous EEG-fMRI. We have the following datasets, if your have any questions, please feel free to contact us.


A test-retest EEG dataset for 2 Resting and 3 subject-driven states
Dowload dataset from OpenNeuro

Here we present a test-retest EEG dataset acquired at 2 resting (eyes open and eyes closed) and 3 subject-driven cognitive states (memory, music, subtraction) with both short-term (within 90 mins) and long-term (one-month apart) designs. 90 participants were recorded during three EEG sessions, with 64 channels and sample frequency 500 Hz. Each session includes EEG and behavioral data along with rich samples of behavioral assessments testing demographic, sleep, emotion, mental health and the content of self-generated thoughts (mind wandering). This data enables the investigation of both intra- and inter-session variability not only limited to electrophysiological changes, but also including alterations in resting and cognitive states, at high temporal resolution. Also, this dataset is expected to add contributions to the reliability and validity of EEG measurements with open resource.
Please cite our following information if you use our data:
1. Yulin Wang and Wei Duan and Debo Dong and Lihong Ding and Xu Lei* (2022). A test-retest resting and cognitive state EEG dataset. OpenNeuro. [Dataset] doi: doi:10.18112/openneuro.ds004148.v1.0.1
2. Yulin Wang, Wei Duan, Debo Dong, Lihong Ding and Xu Lei* (2022). A test-retest resting, and cognitive state EEG dataset during multiple subject-driven states. Scientific Data. 9: 566.
1. Wei Duan, Ya-Jie Wang, Xinyuan Chen, Wenrui Zhao , Hong Yuan, Xu Lei*. Reproducibility of power spectrum, functional connectivity and network construction in resting-state EEG. Journal of Neuroscience Methods. Jan 2021 384: 108985.
2. Lihong Ding, Wei Duan, Yulin Wang, Xu Lei*. Test-retest Reproducibility Comparison in Resting and the Mental Task States : A Sensor and Source-level EEG Spectral Analysis. International Journal of Psychophysiology. Mar 2022 173: 20-28.
在本数据集包括记录了90位被试,在2个静息状态(睁眼和闭眼)和3个自我驱动的思维状态(回忆当天经历、默默哼唱音乐、减法)下获得的脑电数据。5种条件都分别持续了5分钟,电极数64,采样率500 Hz。采用了测试-再测试实验设计,在第一次实验完成后,短期(90分钟内)和长期(间隔一个月)分别进行重复测验。每次实验中都包括脑电和行为数据,以及丰富的量表信息,包括人口统计学信息、睡眠、情绪、心理健康和自我产生的思维内容(如:迷你纽约认知问卷,阿姆斯特丹静息态问卷)。这一数据使得研究不同测试间的变异性成为可能。通过本数据集的开放获取,我们希望增强静息态和状态EEG的可靠性和有效性。


A Resting-state EEG Dataset for Sleep Deprivation
Dowload dataset from OpenNeuro

We present an eyes-open resting-state EEG dataset to investigate the impact of sleep deprivation on mood, alertness, and resting-state EEG. The dataset comprises EEG recordings and cognitive data from 71 participants undergoing two testing sessions: one involving sleep deprivation and the other normal sleep. In each session, participants engaged in eyes-open resting-state EEG. The Psychomotor Vigilance Task (PVT) was employed for alertness measurement. Emotional and sleepiness were measured using Positive and Negative Affect Scale (PANAS) and Stanford Sleepiness Scale (SSS). Additionally, to examine the influence of individual sleep quality and traits on sleep deprivation, Pittsburgh Sleep Quality Index (PSQI) and Buss-Perry Aggression Questionnaire were utilized. This dataset's sharing may contribute to open EEG measurements in the field of sleep deprivation.
Please cite our following information if you use our data:
1. Chuqin Xiang and Xinrui Fan and Duo Bai and Ke Lv and Xu Lei* (2023). A Resting-state EEG Dataset for Sleep Deprivation.. OpenNeuro. [Dataset] doi: doi:10.18112/openneuro.ds004902.v1.0.2



A small dataset for resting-state EEG
Dowload dataset from here
Code is u8nx
This small dataset was shared for EEG beginner, they may used this for data practice (or data proprecess). It was created by Sleep and NeuroImaging Center. This data set consists of 20 four-minute EEG recordings, obtained from 10 volunteers.
Experiment: Both eyes-closed and eyes-open resting-state EEG data were recorded about five minutes from the 64 scalp tin electrodes mounted in an elastic cap (Brain Products, Munich, Germany), with the sampling frequency of 500 Hz around 9:00 to 12:00 in the morning. The impedance of all electrodes was kept below 5 kΩ. Subject was introduced to eyes-closed first, and then eyes-open.
Preprocessing: The preprocessing was conducted using MATLAB scripts supported by EEGLAB. The continuous EEG data were down-sampled to 100 Hz and digitally filtered within the 0.1-45 Hz frequency band using a Chebyshev II-type filter. The filtered EEG recordings were re-referenced to average reference, and then epoched to 2 s. The segmentations with ocular, muscular, and other types of artifact were identified and excluded. We only retained the first 120 segmentations, constituting a four-munute EEG recording for each subject.