Discover datasets

This dataset contains simultaneous scalp and ear-EEG recordings from six healthy right-handed participants performing cued left- and right-hand fist clenching (motor execution). It comprises 1114 trials recorded with a Neuroscan SynAmps2 system at 1000 Hz. The dataset was designed to investigate in-ear sensing for motor task classification and for brain-computer interface benchmarking. It has been reformatted into BIDS with HED annotations via MOABB.

Participants

6

Channels

122 (10-05)

Citations

12

HED

v8.4.0

Size

18.4 GB

Version

v1.0.4

Updated

Oct 19, 2026

EEG recordings from 14 healthy, BCI-naive participants performing asynchronous voluntary index-finger tapping (right, left) and rest, originally reported by Wairagkar et al. (2018). Data were recorded at 1024 Hz from 19 scalp channels and preprocessed with filtering and ICA artifact removal. Trials are segmented around movement onset to study neural correlates of movement intention, and the dataset is distributed in BIDS format via MOABB.

Participants

14

Channels

19 (10-20)

Citations

12

HED

v8.4.0

Size

1.23 GB

Version

v1.0.2

Updated

Aug 18, 2026

EEG recordings from 12 healthy, BCI-naive participants performing two-class motor imagery (sustained right-hand palmar grip versus both-feet plantar extension) across two sessions, with 200 trials per session. Data were recorded from 13 sensorimotor channels at 512 Hz with visual cursor feedback, in support of an autocalibrating, recurrently adapting online ERD-based brain-computer interface. The dataset is distributed in BIDS format via MOABB.

Participants

12

Channels

13 (10-10)

Citations

94

HED

v8.4.0

Size

2.83 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains EEG recordings from 9 healthy subjects performing cued motor imagery of left hand, right hand, feet, and tongue movements, known as BCI Competition IV Dataset 2a. Each subject completed two sessions of six runs, with 22 EEG and 3 EOG channels sampled at 250 Hz. It is widely used as a benchmark for multi-class motor imagery classification in brain-computer interface research.

Participants

9

Channels

22 (10-10)

Citations

89

HED

v8.4.0

Size

1.38 GB

Version

v1.0.3

Updated

Oct 8, 2026

EEG recordings from 8 healthy participants performing cue-based motor imagery of the right hand, feet, and rest, with 20 trials per class. The data were collected for the PhD thesis of A. Barachant on robust control of an effector through an asynchronous EEG brain-machine interface, and correspond to Step B (cue-based imagery without feedback) of the Brain Switch campaign. They are intended for familiarization and algorithm development, including Riemannian geometry and CSP-based classification.

Participants

8

Channels

16 (10-20)

Citations

1

HED

v8.4.0

Size

232 MB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains 19-channel EEG recordings from 7 healthy participants performing classical motor imagery of the left hand, right hand, and passive rest. Participants responded to visual arrow cues in offline sessions, without feedback. The recordings were made with a Nihon Kohden EEG-1200 system at 200 Hz. The data support brain-computer interface research and motor imagery classification benchmarking.

Participants

7

Channels

19 (10-20)

Citations

82

HED

v8.4.0

Size

1.20 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains 64-channel EEG recorded at 1000 Hz from 31 healthy participants performing a row/column P300 speller task with 4L and 5L grid sizes. Each participant completed three sessions. It is the P300 component of a multimodal eye-BCI collection that also includes eye-tracking and high-speed video for analyzing ocular activity across BCI paradigms. Events are annotated with HED tags as Target and NonTarget flashes.

Participants

31

Channels

62 (10-10)

Citations

16

HED

v8.4.0

Size

40.7 GB

Version

v1.0.4

Updated

Oct 8, 2026

EEG recordings from 9 healthy, BCI-naive participants performing two-class motor imagery (left vs. right hand), known as BCI Competition IV Dataset 2b (Graz data set B). Each participant completed five sessions: two screening sessions without feedback and three sessions with smiley feedback. Three bipolar EEG channels (C3, Cz, C4) were recorded at 250 Hz. This deposit is a MOABB-derived BIDS conversion of the original data.

Participants

9

Channels

3 (10-20)

Citations

81

HED

v8.4.0

Size

663 MB

Version

v1.1.0

Updated

Oct 15, 2026

This dataset contains 32-channel EEG recordings from 12 healthy participants performing three-class motor imagery of the same (right) upper extremity: rest, imagined hand grasping, and imagined elbow flexion. Each participant completed 4 sessions of 60 trials (20 per class), recorded with an EGI Geodesic Net Amps system at 1000 Hz. The data support brain-computer interface research on decoding closely related movements of a single limb, for example in rehabilitation and motor control.

Participants

12

Channels

32 (egi-geodesic)

Citations

46

HED

v8.4.0

Size

52.0 GB

Version

v1.0.4

Updated

Oct 19, 2026

A multimodal EEG, EOG and EMG dataset from 25 healthy, BCI-naive right-handed participants performing motor imagery and real movement of 11 intuitive upper-limb tasks. The tasks were 6 reaching directions, 3 grasping types and 2 wrist-twisting movements. Each participant completed 3 sessions of 550 trials. Recordings used 60 EEG channels at 1000 Hz. This BIDS derivative was generated via MOABB and is intended for BCI research on motor control and prosthetics.

Participants

25

Channels

71 (10-10)

Citations

90

HED

v8.4.0

Size

326 GB

Version

v1.0.2

Updated

Aug 18, 2026