Discover datasets

EESM19: Ear-EEG Sleep Monitoring data set This data set was collected as part of development and quality assessment of the ear-EEG as a sleep monitoring platform. Data collection took place between 2018 and 2020. First publication was in 2019 (https://doi.org/10.1038/s41598-019-53115-3), hence the '19' in the name. The data set consists of 2 parts (a & b): a: 20 subjects who each spent 4 nights sleeping with a partial PSG (EEG, EOG and chin EMG electrodes), ear-EEG and a wristworn actigr

Participants

20

Size

268 GB

Updated

Jun 6, 2026

Ear-EEG Sleep Monitoring 2023 (EESM23) data set **Overview** This dataset was collected as part of a research project on ear-EEG sleep monitoring which took place in 2020-2022. The data set contains nightly EEG recordings from 10 healthy participants ('subjects'). The first two recordings consist of polysomnogrpahy (PSG) measurements and ear-EEG measurements. The remaining ten recordings consist of only ear-EEG measurements, though a few subjects were asked to repeat a recording. Only t

Participants

10

Size

25.7 GB

Updated

Jun 6, 2026

# Chisco Dataset This dataset is a Chinese imagined speech dataset with five participants, identified as sub-01 to sub-05. The dataset includes raw data and preprocessed data in both fif and pkl formats. Information also can be found in https://github.com/zhangzihan-is-good/Chisco ## Supplementary Information The initial dataset release encompassed data from three participants (sub-01 to sub-03) as detailed in related Chisco publications. Subsequently, data from two additional subjects (sub-0

Participants

5

Size

90.7 GB

Updated

Jun 6, 2026

In this dataset we included iEEG recordings of responses to 115 intracranial high frequency stimulations evoking visual hallucinations, in 22 patients undergoing stereo-EEG presurgical evaluation for drug-resistant epilepsy. The dataset contains 21 seconds of iEEG data around each stimulation, 8 seconds before the start of the stimulation, up to 5 seconds of intracranial stimulation and 8 seconds after the end of the stimulation. We have used high-frequency bipolar stimulations of different

Participants

20

Size

4.03 GB

Updated

Jun 6, 2026

# Overview The dataset consists of data from two experiments in which subjects were presented bursts of noise from loudspeakers at different elevations. Subjects who participated in either experiment were initially tested in their ability to localize elevated sound sources. Both experiments were conducted in a hemi-anechoic chamber. Localization Tests Bursts of pink noise were presented from loudspeakers at different elevations and 10° azimuth (to the listeners right). In the localization test

Participants

58

Size

22.3 GB

Updated

Jun 6, 2026

Overview: This is the "Siefert2024" dataset. It is the sleep EEG data from Siefert et al., 2024 (https://doi.org/10.1523/JNEUROSCI.0022-24.2024). In brief, it contains sleep EEG data from 34 participants while Targeted Memory Reactivation was administered. Please cite the following paper: E.M. Siefert, S. Uppuluri, J. Mu., M.C. Tandoc, J.W. Antony, A.C. Schapiro (2024). Memory reactivation during sleep does not act holistically on object memory. Journal of Neuroscience, 10.1523/JNEUROSCI.0022-

Participants

34

Size

9.04 GB

Updated

Jun 6, 2026

Dot Probe Continuous Performance Task in control & sub-acute mild TBI. Published here: https://pubmed.ncbi.nlm.nih.gov/31368085/ For CTL and sub-acute mTBI: Session 1 was from 3 to 14 days post-injury and was the only session with MRI. MRI will be uploaded later (bug issues on upload). Session 2 was ~2 months (1.5 to 3) and Session 3 was ~4 months (3 to 5) following Session 1. There was A LOT of subject attrition over timepoints. Same samples as reported here: https://psycnet.apa.org/r

Participants

91

Size

55.9 GB

Updated

Jun 6, 2026

Main entrance: face_0_main Preprocessing: face_1_prep Decoding: face_2_dec RSA: face_3_rsa Statistical: face_4_stat BMS: face_6_bayes.m During original OPM-MEG data acquisition, individual facial point clouds and structural MRIs were not collected due to the unavailability of optical scanning equipment. Hence, all analyses were conducted at the whole-brain & sensor level. The raw data were originally stored using in-house LabVIEW format and were converted into the FIF format la

Participants

21

Size

27.6 GB

Updated

Jun 6, 2026

Data and code for the paper: Tijl Grootswagers, Genevieve Quek, Zhen Zeng, & Manuel Varlet. 2025. “Human Infant EEG Recordings for 200 Object Images Presented in Rapid Visual Streams.” Scientific Data. https://doi.org/10.1038/s41597-025-04744-z See the linked paper for details. The "code" directory contains all the code to reproduce the figures in the paper. It requires fieldtrip and cosmomvpa, change the paths to these toolboxes at the top of each script (or remove the lines and add them to t

Participants

42

Size

1.24 GB

Updated

Jun 6, 2026

# Overview This is the "Sternberg Difficult" dataset contain RAW EEG data. Raven Progressive Standard Matrices scores for each participant are provided in participants.tsv # Task Participants completed a version of the Sternberg task (Sternberg, 1966; Fig 1) during EEG recording. Stimuli were all consonants of the Russian alphabet letters, except for “щ”[sch] and “й” [ij], presented in sets of 3, 6, 9, 12, and 15 letters. No letter repeated within a set. Each trial was preceded by a 500-1000 m

Participants

48

Size

14.3 GB

Updated

Jun 6, 2026