Discover datasets

A multi-distance, high-density fNIRS dataset recorded over the motor cortex with a lightweight system that includes an accelerometer. Participants completed a resting-state recording, a bilateral ball-squeezing task, and a motion creation task with instructed head and facial movements to deliberately induce motion artifacts. The dataset supports work on short-separation regression, motion artifact correction, and diffuse optical tomography image reconstruction.

Participants

12

Size

572 MB

Version

v1.0.0

Updated

Jun 19, 2026

EEG recordings from healthy participants performing self-initiated palmar and lateral reach-and-grasp actions, acquired simultaneously across three recording systems: gel-based laboratory EEG, water-based mobile EEG, and dry-electrode mobile EEG. The dataset supports investigation of the feasibility of decoding natural reach-and-grasp movement-related cortical potentials across EEG acquisition modalities for brain-computer interface applications. It is distributed in BIDS format as part of the MOABB benchmark collection.

Participants

45

Channels

58 (10-05)

Citations

50

HED

v8.4.0

Size

8.67 GB

Version

v1.0.4

Updated

Oct 8, 2026

EEG recordings from two tetraplegic pilots of team Brain Tweakers (CNBI, EPFL) during longitudinal motor-imagery BCI training for the Cybathlon 2016 BCI race. The dataset contains 16-channel EEG from cue-based offline calibration runs with three classes (both hands, both feet, rest), recorded at 512 Hz. It was re-released in BIDS format from the original Zenodo deposit via MOABB.

Participants

2

Channels

16 (10-10)

HED

v8.4.0

Size

5.30 GB

Version

v1.0.0

Updated

Oct 8, 2026

A multimodal neuroimaging dataset (EEG, anatomical MRI and functional MRI) for studying the spatiotemporal dynamics of visual processing in the human brain, as described by the dataset title. This is a NEMAR-hosted copy of OpenNeuro dataset ds002814. The associated article was under review at the time of the record. The source README is an unfilled placeholder, so further study details are not available in the dataset documentation. Contact the corresponding authors (Seyed-Mahdi Khaligh-Razavi, Fatemeh Ebrahiminia) for more information.

Participants

21

Citations

7

Size

48.6 GB

Version

v1.0.0

Updated

Jun 21, 2026

EEG recordings from 20 healthy participants who wrote ten letters (a, d, e, f, j, n, o, s, t, v) with the right index finger after a visual cue. The dataset supports classification of handwritten characters, either directly from low-frequency and broadband EEG or with a two-step approach that first decodes continuous hand kinematics and then classifies the letter. It is intended for research on non-invasive brain-computer interfaces for communication.

Participants

20

Channels

60 (10-10)

Citations

13

HED

v8.4.0

Size

27.0 GB

Version

v1.0.2

Updated

Aug 18, 2026

UV_EEG is an EEG dataset by Yukako Ito, distributed in BIDS format. It is mirrored on NEMAR from the original OpenNeuro deposit (ds005274). The README provides no further details about participants, experimental paradigm, or acquisition.

Participants

22

Channels

6 (10-10)

Size

71.9 MB

Version

v1.0.0

Updated

Jun 26, 2026

This dataset contains 32-channel EEG recorded from 22 healthy participants performing visual imagery of ten cued items: animals (dog, bird, fish), geometric figures (pentagram, square, circle), and objects (scissor, watch, cup, chair). Each participant completed up to two sessions of three runs, giving 16,800 trials in total. It is intended for developing visual imagery-based brain-computer interfaces and is distributed in BIDS format with HED event annotations.

Participants

22

Channels

32 (10-10)

Citations

3

HED

v8.4.0

Size

75.4 GB

Version

v1.0.3

Updated

Aug 20, 2026

A longitudinal motor imagery EEG dataset from 18 BCI-naive participants recorded across six sessions (one offline, five online) with 22 EEG channels at 512 Hz. It supports the study of transfer learning for brain-computer interface skill acquisition, comparing unsupervised Generic Recentering and supervised Personally Assisted Recentering for calibration-free training. Participants performed left/right hand motor imagery with continuous visual feedback.

Participants

18

Channels

22 (10-10)

Citations

53

HED

v8.4.0

Size

14.4 GB

Version

v1.0.5

Updated

Oct 8, 2026

The Multimodal Resource for Studying Information Processing in the Developing Brain (MIPDB) provides high-density 128-channel EEG and eye-tracking recordings from a developmental cohort spanning childhood to adulthood. Participants completed a battery of cognitive and perceptual paradigms, including resting state, surround suppression, naturalistic viewing, contrast change detection, sequence learning, and symbol search. This BIDS conversion contains EEG data from 111 participants, with source eye-tracking and behavioural data preserved in sourcedata.

Participants

111

Citations

65

Size

110 GB

Version

v1.0.0

Updated

Jun 17, 2026

EEG recordings from 8 healthy participants performing self-initiated reaching movements with either the right arm alone (unilateral) or both arms synchronously (bilateral). Data were recorded with a 64-channel wireless active-electrode system and support research on movement intention recognition for exoskeleton-supported stroke rehabilitation. This BIDS re-release was generated via MOABB from the original Zenodo deposit.

Participants

8

Channels

64 (10-10)

HED

v8.4.0

Size

1.49 GB

Version

v1.0.0

Updated

Oct 8, 2026