Discover datasets

EEG recordings from 25 healthy, BCI-naive right-handed participants performing motor imagery of two joints of the same limb (right hand and right elbow). Each participant completed 15 sessions over 3 days, giving 600 trials with 62-channel EEG sampled at 1000 Hz plus EOG. The dataset supports research on decoding fine-grained motor imagery within the same limb for motor rehabilitation and prosthetic control BCIs.

Participants

25

Channels

62 (10-10)

Citations

51

HED

v8.4.0

Size

53.8 GB

Version

v1.0.2

Updated

Aug 18, 2026

EEG dataset from 12 healthy participants performing an offline 4-class code-modulated visual evoked potential (c-VEP) brain-computer interface task. The study compares novel burst c-VEP sequences with traditional m-sequences at two stimulus amplitude depths (100% and 40%). The aim is to optimize classification accuracy, minimize calibration data, and improve user experience. Data were recorded with a 32-channel BrainProducts LiveAmp system at 500 Hz.

Participants

12

Channels

32 (10-10)

Citations

18

HED

v8.4.0

Size

160 MB

Version

v1.0.0

Updated

May 11, 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

EEG recordings from 20 healthy participants who wrote ten letters (a, d, e, f, j, n, o, s, t, v) with the right index finger after a visual cue. The dataset supports classification of handwritten characters, either directly from low-frequency and broadband EEG or with a two-step approach that first decodes continuous hand kinematics and then classifies the letter. It is intended for research on non-invasive brain-computer interfaces for communication.

Participants

20

Channels

60 (10-10)

Citations

13

HED

v8.4.0

Size

27.0 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains 62-channel EEG recordings from 18 healthy, right-handed participants performing motor imagery of eight multi-joint movements of the unilateral upper limb. The movements involve the hand, wrist, elbow and shoulder. Each participant completed 320 cue-based trials across 8 blocks, with 40 trials per class. The data support research on brain-computer interfaces for motor rehabilitation.

Participants

18

Channels

62 (10-10)

Citations

6

HED

v8.4.0

Size

50.0 GB

Version

v1.0.2

Updated

Aug 18, 2026

EEG motor imagery dataset recorded from 50 acute stroke patients (1-30 days post-stroke) using a wireless portable semi-dry saline EEG system. Patients imagined left- or right-hand grasping during video-guided trials (40 per subject). It is presented as the first open dataset addressing left- and right-handed motor imagery in acute stroke, supporting BCI algorithm development and rehabilitation applications. Raw and preprocessed data and patient clinical characteristics are provided.

Participants

50

Channels

29 (10-10)

Citations

88

HED

v8.4.0

Size

1.15 GB

Version

v1.0.3

Updated

Aug 18, 2026

A hybrid EEG-fNIRS motor imagery dataset for brain-computer interface research in intracerebral hemorrhage (ICH). It includes 37 participants (17 healthy, 20 ICH patients) performing two-class left/right hand grasping motor imagery across up to several sessions. The data are pre-epoched, and the dataset is intended for developing and benchmarking BCI approaches for stroke rehabilitation.

Participants

37

Channels

32 (10-10)

Citations

6

HED

v8.4.0

Size

2.59 GB

Version

v1.0.0

Updated

May 11, 2026

EEG recordings from 12 healthy, BCI-naive participants performing kinesthetic motor imagery of upper-limb, elbow-centered tasks. The paradigm has 10 classes: rest, elbow flexion, and eight goal-directed daily-living tasks such as drawer opening, soup eating, weight lifting and combing. Data were recorded with a 17-channel EGI system at 1000 Hz. The dataset supports research on the versatility of EEG models for motor-imagery BCI control and rehabilitation.

Participants

12

Channels

17 (other)

Citations

12

HED

v8.4.0

Size

4.18 GB

Version

v1.0.3

Updated

Aug 18, 2026

M3CV is a multi-subject, multi-session, multi-task EEG database with 64-channel recordings from 95 healthy young adults. Each subject was recorded on two different days across six paradigms covering 13 distributed signal types: resting states, motor execution, transient visual, auditory and somatosensory responses, SSVEP, P300, SSAEP and SSSEP. The data were originally released for an EEG-based biometrics competition on Kaggle. This BIDS version reconstructs pseudo-continuous EEG from the distributed pre-epoched, preprocessed 250 Hz files.

Participants

95

Channels

64 (10-10)

Citations

31

Size

21.6 GB

Version

v1.0.0

Updated

May 11, 2026

This dataset contains 32-channel EEG recorded with a BioSemi ActiveTwo system from 12 healthy participants using a single-flicker, spatially coded SSVEP brain-computer interface for spatial navigation. A single 15 Hz flickering square is shown at the screen center, and four non-flickering targets lie at the cardinal directions. Gaze direction produces distinct spatial topographies of the SSVEP response. Two sessions per subject are included: a structured training calibration session and an online adaptive BCI game session.

Participants

12

Channels

32 (10-10)

Citations

52

HED

v8.4.0

Size

10.9 GB

Version

v1.0.2

Updated

Aug 18, 2026