Discover datasets

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

A P300 brain-computer interface dataset comprising EEG recordings from 8 subjects (4 disabled, 4 able-bodied) performing a visual oddball task with image stimuli. The dataset includes 32-channel EEG data sampled at 2048 Hz from 4 sessions per subject, with 810 total trials (135 targets, 675 non-targets) classified using Fisher's linear discriminant analysis and Bayesian linear discriminant analysis. This derivative dataset demonstrates high classification accuracy (100%) and information transfer rates suitable for BCI applications in disabled populations.

Participants

8

Channels

32 (10-10)

Citations

82

HED

v8.4.0

Size

4.08 GB

Version

v1.0.2

Updated

Aug 18, 2026

A lower-limb motor imagery EEG dataset comprising 30 knee pain patients performing left and right leg flexion/extension imagery tasks across 5 sessions. The dataset contains 500 trials (250 per class) recorded at 500 Hz from 30 EEG channels using a standard 10-05 montage. This clinical dataset supports the development and evaluation of brain-computer interface classifiers for motor rehabilitation applications in patients with knee pain.

Participants

30

Channels

30 (10-10)

Citations

3

HED

v8.4.0

Size

19.4 GB

Version

v1.0.2

Updated

Aug 18, 2026

A motor imagery EEG dataset comprising 31 healthy participants performing left and right hand grasping imagery tasks across three sessions. The dataset includes 2,520 trials recorded at 1000 Hz using a 64-channel Neuroscan system with standardized electrode montage, designed for brain-computer interface research and motor control applications.

Participants

31

Channels

66 (10-10)

Citations

11

HED

v8.4.0

Size

30.3 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 15 healthy subjects performing a P300-based brain-computer interface task for home appliance control. The experiment employed a visual oddball paradigm with 31-channel EEG acquisition at 500 Hz, yielding target and non-target event classifications across 50 training and 30 testing trials per subject. The data supports the development and benchmarking of P300-based BCI systems for practical applications in smart home environments.

Participants

15

Channels

31 (10-10)

Citations

8

HED

v8.4.0

Size

1.22 GB

Version

v1.0.3

Updated

Aug 18, 2026