Discover datasets

HEFMI-ICH is a hybrid EEG-fNIRS motor imagery dataset designed for brain-computer interface applications in intracerebral hemorrhage rehabilitation. The dataset comprises 37 participants (17 healthy controls and 20 ICH patients) performing 2-class hand motor imagery tasks (left and right hand grasping) across 1-6 sessions per subject. With 32-channel EEG recordings at 256 Hz and approximately 3,330 trials, this dataset supports the development and evaluation of BCI systems for clinical stroke rehabilitation.

Participants

37

Size

2.58 GB

Version

v1.0.1

Updated

Jun 3, 2026

An open-access electroencephalography dataset of motor imagery brain-computer interface performance under six different distraction conditions. Sixteen healthy participants performed left versus right hand motor imagery while experiencing flickering video, number search tasks, news listening, eyes closed state, vibro-tactile stimulation, or no distraction. The dataset comprises 504 trials per subject acquired across one calibration run and six feedback runs, providing a resource for investigating the robustness of motor imagery-based BCIs under realistic cognitive load.

Participants

16

Size

61.6 GB

Version

v1.0.1

Updated

Jun 3, 2026

This dataset comprises EEG and EMG recordings from 15 healthy participants performing emergency braking tasks during simulated driving at 200 Hz sampling rate. Participants drove a virtual racing car following a computer-controlled lead vehicle, with occasional abrupt decelerations requiring immediate emergency braking responses. The study demonstrates the feasibility of predicting driver braking intentions from neural and muscular signals, with established applications in neuroergonomics and driver assistance systems. Currently 15 of 18 subjects are available.

Participants

15

Size

4.01 GB

Version

v1.0.0

Updated

Jun 3, 2026

A 32-channel EEG dataset comprising visual imagery recordings from 22 healthy participants performing mental imagery tasks of 10 distinct categories (animals, geometric figures, and objects). The dataset contains 16,800 trials collected across 2 sessions per subject with simultaneous EEG acquisition at 1000 Hz, designed to support brain-computer interface research and classification algorithm development.

Participants

22

Size

31.7 GB

Version

v1.0.0

Updated

Jun 3, 2026

The BNCI 2015-009 AMUSE dataset comprises EEG recordings from 21 healthy subjects performing an auditory oddball task using spatial hearing as a discriminating cue. The dataset implements a P300-based brain-computer interface paradigm with multi-class auditory stimuli presented from five spatially distributed speakers at varying inter-stimulus intervals. Preprocessed data includes 60 EEG channels and 2 EOG channels sampled at 100 Hz (downsampled from 250 Hz acquisition rate), with offline classification achieving up to 100% accuracy on best-performing individual subjects.

Participants

21

Size

4.57 GB

Version

v1.0.0

Updated

Jun 3, 2026

This dataset comprises EEG recordings from 16 healthy participants performing motion visually evoked potential (mVEP) speller tasks using three different paradigm variants: overt gaze-directed, covert attention-based, and foveal motion center stimulation. The study investigates gaze-independent brain-computer interface communication through P300-based visual speller applications with two-level hierarchical selection from 6 groups/symbols per level, including copy-spelling and free-spelling conditions.

Participants

16

Size

1.27 GB

Version

v1.0.0

Updated

Jun 3, 2026

Participants

15

Size

8.65 GB

Version

v1.0.0

Updated

Jun 1, 2026

Participants

13

Size

3.08 GB

Version

v1.0.0

Updated

Jun 2, 2026

Participants

12

Size

2.06 GB

Version

v1.0.0

Updated

Jun 2, 2026

Participants

11

Size

4.38 GB

Version

v1.0.0

Updated

Jun 2, 2026