Discover datasets

License

License filtering isn't available while searching — clear the search box to filter by license.

Participants

Features

Has HED annotation
Has viewer
Has data quality Coming soon

The viewer and Zarr filters aren't available while searching — clear the search box to use them.

Channels & montage

Channel density Electrode system

Channel and montage filters apply while browsing — clear the search box to use them.

Reset

nm000291
Pan2023: Cross-session motor imagery dataset from Pan et al. 2023

EEG

A cross-session motor imagery EEG dataset from 14 healthy participants who performed cued left- versus right-hand motor imagery on two separate days. Data were recorded with 28 EEG channels at 250 Hz and comprise 60 trials per class per session. The dataset supports research on brain-computer interface decoding and cross-session variability, and it is distributed here in BIDS format with HED event annotations.

Participants

14

Channels

28 (10-10)

HED

v8.4.0

Size

1018 MB

Version

v1.0.0

Updated

Oct 8, 2026

by Lincong Pan et al.
nm000323
Lee et al. 2019 (ERP) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy

EEG Zarr

…copy-spell 'PATTERN RECOGNITION MACHINE LEARNING' (36 characters). Each character received 5…

Participants

54

Channels

62 (10-05)

Citations

100

HED

v8.4.0

Size

132 GB

Version

v1.0.5

Updated

Oct 8, 2026

by Min-Ho Lee et al.
nm000199
Learning from label proportions for a visual matrix speller (ERP)

EEG Zarr

EEG recordings from 13 healthy participants performing a copy-spelling task with a visual 6x7 matrix ERP speller. The speller was designed for learning from label proportions (LLP), an unsupervised, calibrationless classification approach for ERP-based brain-computer interfaces. Two stimulus sequences with different target/non-target ratios allowed LLP to reconstruct mean target and non-target responses without labeled data. In the original online study, 84.5% of characters were spelled correctly on average without prior calibration.

Participants

13

Channels

31 (10-10)

Citations

46

HED

v8.4.0

Size

5.15 GB

Version

v1.0.3

Updated

Oct 8, 2026

by David Hübner et al.
on003004
Imagined Emotion Study

EEG Zarr

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003004-blue…

Participants

34

Channels

224 (biosemi)

Citations

73

Size

36.0 GB

Version

v1.0.0

Updated

Jun 21, 2026

by Julie Onton et al. OpenNeuro ds003004
nm000173
Motor Imagery ataset from Ofner et al 2017

EEG Zarr

EEG recordings from fifteen healthy participants who executed and imagined six sustained upper limb movements (elbow flexion/extension, forearm supination/pronation, hand open/close) plus a rest condition, in two separate sessions. The dataset supports decoding of single upper limb movements from the time-domain of low-frequency EEG. It is relevant to non-invasive control of motor neuroprostheses and robotic arms. This BIDS version was generated from the original BNCI Horizon 2020 data using MOABB.

Participants

15

Channels

61 (10-05)

Citations

93

HED

v8.4.0

Size

34.0 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Patrick Ofner et al.
on004011
The nature of neural object representations during dynamic occlusion

MEG

Raw and preprocessed magnetoencephalography (MEG) recordings from participants viewing objects that moved along trajectories and were either occluded or disappeared. The dataset supports investigation of how the brain represents objects during dynamic occlusion. It includes behavioural and eye-tracking data as source data, and preprocessed epochs in CoSMoMVPA format.

Participants

22

Citations

12

Size

258 GB

Version

v1.0.0

Updated

Jun 18, 2026

by Lina Teichmann et al. OpenNeuro ds004011
on003421
HD-EEGtask(Dataset 2)

EEG Zarr fidelity issue

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003421-blue…

Participants

20

Channels

257 (egi-geodesic)

Citations

1

Size

76.8 GB

Version

v1.0.0

Updated

Jun 21, 2026

by Ahmad Mheich et al. OpenNeuro ds003421
on007012
FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images

BEH EEG Zarr

FOODEEG is an open dataset of electroencephalographic (EEG) and behavioural responses to food images from 117 participants. In one session, EEG was recorded during a food categorisation task in which participants judged images as healthy, tasty, or something they were willing to eat. In a second session, participants completed a food go/no-go task, a paired choice task, and questionnaires on dietary style and motivation. The dataset also includes raw and cleaned EEG data and normative ratings of the food stimuli from online samples.

Participants

117

Size

91.1 GB

Version

v1.0.0

Updated

Jun 29, 2026

by Chae et al. OpenNeuro ds007012
on006171
EEG data during three near-threshold visual detection tasks: a no-cue task, a noninformative cue task (50% validity), and an informative cue task (100% validity)

EEG Zarr

EEG recordings from participants performing three near-threshold visual detection tasks that differed in cue information: a no-cue task, a noninformative cue task (50% validity), and an informative cue task (100% validity). The dataset was collected to study how prestimulus alpha-band activity relates to perception of near-threshold visual stimuli. It is shared in BIDS format.

Participants

36

Channels

128 (10-05)

Citations

8

Size

67.8 GB

Version

v1.0.0

Updated

Jun 28, 2026

by María Melcón et al. OpenNeuro ds006171
on005872
EEGEyeNet Dataset

EEG Zarr

A BIDS-standardized example dataset of simultaneously recorded EEG and eye-tracking data from a single subject, drawn from the EEGEyeNet resource. EEGEyeNet was designed as a dataset and benchmark for eye movement prediction from EEG. This version reformats the original Open Science Framework data to the BIDS standard.

Participants

1

Channels

129 (egi-geodesic)

Citations

7

Size

43.6 MB

Version

v1.0.0

Updated

Jun 28, 2026

by Martyna Beata Płomecka et al. OpenNeuro ds005872