Discover datasets

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

This dataset comprises EEG recordings from 10 patients with spinal cord injury performing five distinct hand and forearm motor imagery tasks: hand open, palmar grasp, lateral grasp, pronation, and supination. The study investigates the decoding of attempted movements from low-frequency EEG signals (movement-related cortical potentials) to support neuroprosthetic applications for upper limb control. Data include 360 trials (72 per class) acquired at 256 Hz with 61 EEG channels and 3 EOG channels, preprocessed with ICA-based artifact rejection and bandpass filtering.

Participants

10

Channels

61 (10-05)

Citations

97

HED

v8.4.0

Size

4.51 GB

Version

v1.0.2

Updated

Aug 18, 2026

A motor imagery brain-computer interface dataset comprising EEG recordings from 30 healthy participants performing left-hand grasping imagery and rest tasks, augmented with pupillometry measurements. The dataset includes 32-channel EEG data sampled at 512 Hz acquired using BioSemi ActiveTwo hardware, with two experimental runs per subject containing 50 trials total. This derivative dataset was processed using the Mother of All BCI Benchmarks (MOABB) framework and is designed to support BCI research and algorithm development.

Participants

30

Channels

32 (10-10)

Citations

37

HED

v8.4.0

Size

3.22 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains EEG recordings from 10 healthy, right-handed participants performing simple and compound limb motor imagery tasks, including imagined movements of the left hand, right hand, both hands, feet, and combined hand-foot movements, as well as rest periods. The data were collected using a 60-channel EEG system (plus EOG channels) at Tianjin University to study EEG oscillatory patterns and cognitive processes underlying motor imagery, and have been converted to BIDS format with HED event annotations by the MOABB project.

Participants

10

Channels

60 (10-10)

Citations

94

HED

v8.4.0

Size

5.64 GB

Version

v1.0.2

Updated

Aug 18, 2026