Discover datasets

A multimodal EEG dataset of motor imagery (MI) recorded from 31 healthy participants over up to three sessions each. Participants imagined left- and right-hand grasping in response to visual cues while 64-channel EEG plus EOG were recorded at 1000 Hz. This is a MOABB-derived version of the Eye-BCI dataset, which also includes eye-tracking and high-speed video for ocular activity analysis across BCI paradigms.

Participants

31

Channels

66 (10-10)

Citations

13

HED

v8.4.0

Size

4.65 GB

Version

v1.0.0

Updated

Jun 3, 2026

EEG recordings from 10 healthy participants performing an online P300 row-column speller task with a 6x6 character matrix. The study compared two stimulus types, Famous Faces and Inverting, in combination with language models for online classification. Data were recorded from 32 channels at 256 Hz across two sessions per subject with three runs each, and are annotated with HED tags.

Participants

10

Channels

32 (10-10)

Citations

28

HED

v8.4.0

Size

773 MB

Version

v1.0.5

Updated

Oct 8, 2026

EEG recordings from 20 healthy participants performing a discrete center-out reaching task in four directions (up, down, left, right) at two speeds (slow, fast) and two distances (near, far). The data were recorded with 67 channels, including EOG, and preprocessed with eye artifact correction (SGEYESUB) and ICA. The dataset supports research on low-frequency EEG encoding of reaching kinematics and on brain-computer interfaces.

Participants

20

Channels

67 (10-05)

Citations

4

HED

v8.4.0

Size

15.4 GB

Version

v1.0.2

Updated

Aug 18, 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 dataset from twenty-one participants performing a continuous feedback learning task in which they predicted the final level of an animated rising bar. Cues (gnomes) differed in how predictable the outcome was (highly predictable, somewhat predictable, unpredictable), enabling study of feedback processing under varying outcome uncertainty. Points were awarded according to the accuracy of each guess.

Participants

21

Channels

31 (10-10)

Citations

1

Size

3.48 GB

Version

v1.0.0

Updated

Jun 23, 2026