Discover datasets

This dataset comprises EEG recordings from 6 healthy subjects performing an error-related potential (ErrP) monitoring task where participants observe an autonomous cursor moving toward targets with controlled error rates (20-40%). The study demonstrates that error-related brain potentials can be detected and decoded during passive monitoring of external agents without direct user control, providing foundational evidence for error monitoring in brain-computer interface applications.

Participants

6

Channels

64 (10-10)

Citations

83

HED

v8.4.0

Size

5.21 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains 62-channel EEG recordings from 25 healthy, right-handed subjects performing kinesthetic motor imagery of the right hand versus right elbow, a within-limb discrimination paradigm. Each subject completed 15 sessions over 3 days, yielding 600 trials total, designed to study brain-computer interface (BCI) decoding of different joint movements from the same limb. The dataset supports research into motor rehabilitation and prosthetic control applications using EEG-based BCI systems.

Participants

25

Channels

62 (10-10)

Citations

52

HED

v8.4.0

Size

53.8 GB

Version

v1.0.2

Updated

Aug 18, 2026

Participants

12

Channels

32 (10-10)

Citations

21

HED

v8.4.0

Size

158 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises preprocessed electroencephalography (EEG) recordings from 20 healthy participants performing a motor imagery task involving discrete reaching movements in four directions (up, down, left, right) with varying speeds and distances. Participants executed 960 trials across 10 blocks while viewing visual cues, with concurrent eye-tracking and motion capture data. The dataset includes extensive preprocessing with artifact correction, source localization, and classification features, making it suitable for brain-computer interface research and motor imagery decoding studies.

Participants

20

Channels

67 (10-05)

Citations

3

HED

v8.4.0

Size

15.4 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 20 healthy participants performing motor imagery of handwritten letter production. Participants imagined writing ten different letters (a, d, e, f, j, n, o, s, t, v) using their right index finger in response to visual cues. The study investigates brain-computer interface applications through direct EEG classification and continuous kinematic decoding approaches, achieving classification accuracies of 26.2% for ten-letter and 46.7% for five-letter tasks.

Participants

20

Channels

60 (10-10)

Citations

12

HED

v8.4.0

Size

27.0 GB

Version

v1.0.2

Updated

Aug 18, 2026

A multi-joint upper-limb motor imagery EEG dataset comprising 18 healthy subjects performing eight distinct imagery tasks involving hand, wrist, elbow, and shoulder movements. The dataset contains 320 trials per subject acquired at 1000 Hz using 62-channel EEG with visual cue-based paradigm, designed for brain-computer interface research and motor rehabilitation applications.

Participants

18

Channels

62 (10-10)

Citations

4

HED

v8.4.0

Size

50.0 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises electroencephalography (EEG) recordings from 50 acute stroke patients (1-30 days post-stroke) performing motor imagery tasks of left- and right-handed hand-grip movements. Recorded using a wireless 29-channel EEG system at 500 Hz, the dataset includes raw and preprocessed data, representing the first open resource addressing left- and right-handed motor imagery in the acute stroke population. The dataset supports brain-computer interface (BCI) algorithm development and clinical rehabilitation applications.

Participants

50

Channels

29 (10-10)

Citations

78

HED

v8.4.0

Size

1.15 GB

Version

v1.0.3

Updated

Aug 18, 2026

Participants

37

Channels

32 (10-10)

HED

v8.4.0

Size

2.59 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 12 healthy participants performing upper-limb motor imagery tasks centered on elbow movements. Participants executed kinesthetic imagery of nine goal-directed tasks (drawer opening, soup preparation, weight lifting, door opening, plate cleaning, combing, pizza cutting, and pick-and-place operations) plus rest, cued by visual stimuli. The dataset contains 330 trials recorded at 1000 Hz using 17 EEG channels and is designed for brain-computer interface (BCI) research and motor imagery classification studies.

Participants

12

Channels

17 (other)

Citations

13

HED

v8.4.0

Size

4.18 GB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset contains EEG recordings from 12 healthy human subjects performing three-class motor imagery tasks involving the same upper extremity: rest, imagined grasping, and imagined elbow flexion. Data were collected across four sessions per subject using a 32-channel EEG system, and the dataset is a BIDS-formatted derivative of the original study on classifying imaginary motor states using time-domain features. The dataset supports research in brain-computer interfaces (BCI) for motor rehabilitation applications.

Participants

12

Channels

32 (egi-geodesic)

Citations

44

HED

v8.4.0

Size

52.0 GB

Version

v1.0.3

Updated

Aug 20, 2026