Discover datasets

A motor imagery brain-computer interface dataset comprising EEG recordings from 52 healthy subjects performing kinesthetic imagery of left and right hand finger movements. The dataset includes 100-120 trials per class with concurrent EMG recordings, psychological and physiological questionnaires, 3D electrode coordinates, and non-task-related control states. Mean classification accuracy of 67.46% (±13.17%) demonstrates substantial inter-subject variability in motor imagery discriminability.

Participants

52

Channels

64 (10-10)

Citations

91

HED

v8.4.0

Size

16.7 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises electroencephalographic (EEG) recordings from 71 healthy subjects performing a visual P300-based brain-computer interface task using the Brain Invaders videogame. Participants engaged with an oddball paradigm involving pseudo-random flashes of 36 symbols on a grid to elicit P300 event-related potentials. The study evaluated the feasibility of a calibration-less P300 BCI system using 16 dry electrodes, with real-time adaptive classification for online feedback during gameplay. EEG data were recorded using 16 active dry electrodes with up to three game sessions per subject, comprising 9 levels with up to 8 attempts per level.

Participants

64

Channels

16 (10-10)

Citations

18

HED

v8.4.0

Size

3.06 GB

Version

v1.0.3

Updated

Aug 18, 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 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 before behavioral response, with applications to neuroergonomics and driving assistance systems.

Participants

15

Channels

59 (10-10)

Citations

81

HED

v8.4.0

Size

14.2 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains 32-channel EEG recordings from 22 healthy adults performing a visual imagery task involving 10 categories of animals, figures, and objects. Data were collected across two sessions per subject using a Neuracle NeuSenW32 system at 1000 Hz sampling rate, and are formatted according to BIDS for use in brain-computer interface research. The dataset supports evaluation of classification approaches such as CSP and EEGNet for decoding imagined visual categories from EEG signals.

Participants

22

Channels

32 (10-10)

Citations

2

HED

v8.4.0

Size

75.4 GB

Version

v1.0.3

Updated

Aug 20, 2026

A derivative EEG dataset containing checkerboard m-sequence-based code-modulated visual evoked potential (c-VEP) recordings from 16 healthy participants across 8 sessions. This dataset is derived from the source dataset by Martínez-Cagigal et al. (2025) (DOI: 10.71569/7c67-v596) and is described in the publication by Fernández-Rodríguez et al. (2023) in Frontiers in Human Neuroscience. The dataset evaluates the influence of spatial frequency in visual stimuli for brain-computer interface applications, with 16-channel EEG data sampled at 256 Hz during a two-class visual stimulation paradigm.

Participants

16

Channels

16 (10-05)

Citations

6

HED

v8.4.0

Size

1.38 GB

Version

v1.0.4

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 16 healthy participants performing a code-modulated visual evoked potential (c-VEP) brain-computer interface task using p-ary m-sequences. The study investigates non-binary m-sequence stimulation patterns to enhance user comfort in c-VEP-based BCIs. Data were collected across 5 sessions per subject with 8 runs per session at 256 Hz sampling rate using 16 EEG channels, providing a comprehensive resource for BCI paradigm development and evaluation.

Participants

16

Channels

16 (10-05)

Citations

28

HED

v8.4.0

Size

1.71 GB

Version

v1.0.4

Updated

Aug 18, 2026

This dataset contains EEG recordings from 20 healthy participants performing a 7-day longitudinal motor imagery brain-computer interface (BCI) task without feedback. Each subject completed 7 sessions of 6 runs, imagining left hand, right hand, feet, and rest movements, yielding 33,600 trials in total. The dataset was converted to BIDS format from the original study by Zhou et al. (2021) examining the relationship between relative power and motor imagery decoding performance across time and subjects.

Participants

20

Channels

41 (other)

Citations

15

HED

v8.4.0

Size

93.1 GB

Version

v1.0.3

Updated

Aug 20, 2026

This dataset comprises electroencephalographic recordings from 21 healthy subjects performing a visual P300-based brain-computer interface task in two environments: a personal computer and a virtual reality headset. The study compares BCI performance and user experience across these modalities using a 6×6 matrix speller paradigm with 16-channel EEG recordings at 512 Hz sampling rate. The experiment was conducted at GIPSA-lab in 2018 and includes both physiological and subjective outcome measures.

Participants

21

Channels

16 (10-10)

Citations

5

HED

v8.4.0

Size

999 MB

Version

v1.0.4

Updated

Aug 18, 2026

A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing motor imagery and motor execution tasks. The dataset comprises 2,520 trials across 63 sessions with 64-channel EEG recordings at 1000 Hz sampling rate, focusing on left and right hand grasping imagery. This resource supports the development and benchmarking of brain-computer interface (BCI) systems for motor control applications.

Participants

31

Citations

11

HED

v8.4.0

Size

4.65 GB

Version

v1.0.0

Updated

Jul 10, 2026

The BNCI 2015-009 AMUSE dataset comprises EEG recordings from 21 healthy subjects performing an auditory oddball task using spatial hearing cues for brain-computer interface applications. Participants discriminated target and non-target stimuli presented from five spatially distributed speakers with varying inter-stimulus intervals, yielding preprocessed data suitable for P300-based BCI research and offline classification studies.

Participants

21

Channels

60 (10-10)

Citations

160

HED

v8.4.0

Size

16.5 GB

Version

v1.0.2

Updated

Aug 18, 2026