Discover datasets

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

The Munich Motor Imagery dataset comprises electroencephalographic recordings from 10 healthy subjects performing two-class motor imagery tasks (left and right hand) cued by visual arrow stimuli. The dataset includes 128-channel EEG data sampled at 500 Hz with preprocessed signals suitable for brain-computer interface research and benchmarking of spatial filtering and classification methods.

Participants

10

Channels

128 (other)

Citations

74

HED

v8.4.0

Size

5.49 GB

Version

v1.0.2

Updated

Aug 18, 2026

This EEG dataset comprises recordings from 9 individuals with CNS tissue damage (stroke and spinal cord injury) performing five mental imagery tasks: word association, mental subtraction, spatial navigation, and motor imagery of the right hand and feet. Data were collected across two sessions using 30-channel EEG at 256 Hz with visual cue-guided paradigm. The dataset includes preprocessed signals with artifact rejection and is intended for brain-computer interface research and motor imagery classification studies.

Participants

9

Channels

30 (10-10)

Citations

74

HED

v8.4.0

Size

2.74 GB

Version

v1.0.2

Updated

Aug 18, 2026

The BNCI2003_IVa Motor Imagery dataset comprises EEG recordings from 5 healthy subjects performing motor imagery tasks (right hand and feet movements) in response to visual cues. This preprocessed dataset, originally from BCI Competition III, contains 280 trials per subject recorded with 118 EEG channels at 100 Hz sampling rate. The dataset has been extensively used for benchmarking brain-computer interface classification algorithms, particularly for evaluating common spatial patterns and feature combination methods.

Participants

5

Channels

118 (10-05)

Citations

86

HED

v8.4.0

Size

735 MB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset contains simultaneous scalp and ear-EEG recordings from 6 healthy right-handed adults performing a motor execution task involving left and right hand fist clenching, cued by visual arrow stimuli. Recordings were made at 1000 Hz using a 122-channel Neuroscan SynAmps2 system, yielding 1114 trials, and are intended for benchmarking motor task classification algorithms such as EEGNet. The dataset was converted to BIDS format using MOABB and originates from a study investigating the utility of in-ear EEG sensing for motor task classification.

Participants

6

Channels

122 (10-05)

Citations

12

HED

v8.4.0

Size

18.4 GB

Version

v1.0.3

Updated

Aug 20, 2026

This dataset comprises EEG recordings from 9 healthy subjects performing two-class motor imagery tasks (left vs. right hand movements) as part of BCI Competition IV Dataset 2b. Data were collected across five sessions per subject: two screening sessions without feedback and three feedback sessions with visual smiley feedback. The dataset includes 3 bipolar EEG channels (C3, Cz, C4) and 3 EOG channels sampled at 250 Hz, providing a benchmark resource for motor imagery-based brain-computer interface research.

Participants

1

Channels

3 (10-20)

Citations

83

HED

v8.4.0

Size

478 MB

Version

v1.0.2

Updated

Aug 18, 2026