Discover datasets

A derivative 4-class code-VEP brain-computer interface dataset processed from the original Zenodo dataset (10.5281/zenodo.8255618), comparing burst c-VEP and m-sequence stimulation paradigms at two amplitude depths (100% and 40%). The study evaluates classification performance and user experience using 32-channel EEG recorded at 500 Hz from 12 healthy participants. Burst c-VEP achieved superior accuracy of 95.6% (with 52.8s calibration) and 90.5% (with 17.6s calibration), compared to m-sequence performance of 85.0% and 71.4% respectively. CNN-based decoding with 250ms sliding windows enabled these results. This derivative dataset optimizes stimulus design for reactive BCI applications while maintaining visual comfort, with reduced amplitude (40%) showing minimal accuracy loss (94.2%) while substantially improving user experience.

Participants

12

Citations

21

HED

v8.4.0

Size

148 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalographic recordings from 12 healthy participants performing a reactive brain-computer interface task using code-modulated visual evoked potentials (c-VEP). The study introduces an innovative 'Burst c-VEP' paradigm employing short bursts of aperiodic visual flashes at 2-4 Hz, designed to optimize classification performance while reducing calibration requirements and improving visual comfort. Data were collected using a 32-channel BrainProducts LiveAmp system at 500 Hz sampling rate across a factorial design manipulating stimulus pattern (burst vs. m-sequence) and amplitude (100% vs. 40% depth modulation).

Participants

12

Citations

21

HED

v8.4.0

Size

142 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 12 healthy participants performing a 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%) to optimize classification performance while minimizing calibration requirements and enhancing user experience. Data were acquired at 500 Hz using a 32-channel BrainProducts system and processed with convolutional neural network architectures, achieving classification accuracies up to 95.6%.

Participants

12

Citations

21

HED

v8.4.0

Size

148 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises multi-session, multi-task EEG recordings from 15 healthy participants performing resting state and graded difficulty levels of the MATB-II task. Acquired at 500 Hz using 62 active electrodes, the dataset includes 90 trials per participant across two sessions and is designed to support passive brain-computer interface applications and mental workload estimation in neuroergonomic contexts. The dataset is in raw, unpreprocessed state and has been formatted according to BIDS standards for accessibility and reproducibility.

Participants

15

Channels

61 (10-10)

Citations

62

HED

v8.4.0

Size

1.22 GB

Version

v1.0.1

Updated

Jul 10, 2026

A code-modulated Visual Evoked Potential (c-VEP) dataset from 12 healthy participants investigating burst c-VEP as a reactive brain-computer interface paradigm. The study employed a factorial design manipulating stimulus pattern (burst vs. m-sequence) and amplitude depth (100% vs. 40% modulation) to optimize classification performance while improving user visual comfort. EEG data were recorded at 500 Hz from 32 channels using a BrainProducts LiveAmp system during a 4-class classification task with 4 visual targets.

Participants

12

Citations

22

HED

v8.4.0

Size

143 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalographic recordings from 12 healthy subjects during music listening with and without a passive head-mounted display (PHMD). The study employed a resting-state paradigm with 16-channel EEG acquisition at 512 Hz, comparing neural activity across alternating blocks of smartphone-on and smartphone-off conditions. The dataset provides raw, unfiltered EEG data in mat and csv formats, enabling investigation of how passive head-mounted displays and auditory stimuli influence brain electrical activity.

Participants

12

Channels

16 (10-10)

Citations

19

HED

v8.4.0

Size

231 MB

Version

v1.0.1

Updated

Jul 10, 2026

BigP3BCI Study J is a P300-based brain-computer interface dataset comprising EEG recordings from 20 healthy subjects performing a 9x8 character grid speller task. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset with ~267 subjects across 20 studies. The data were acquired at 256 Hz using 16-channel EEG with a standard 10-20 montage and are organized in BIDS format with HED event annotations for standardized analysis and machine learning applications.

Participants

20

Channels

16 (10-10)

HED

v8.4.0

Size

426 MB

Version

v1.0.1

Updated

Jul 10, 2026

A large longitudinal dataset of sensorimotor rhythm-based brain-computer interface (BCI) training in 62 healthy adults. The dataset comprises over 600 hours of EEG recordings collected across up to 11 training sessions per participant, containing more than 250,000 trials of motor-imagery tasks (left hand, right hand, both hands, and rest). This resource enables investigation of BCI learning dynamics and algorithm development for non-invasive neural control applications.

Participants

62

Channels

60 (10-10)

Citations

59

HED

v8.4.0

Size

371 GB

Version

v1.0.1

Updated

Jul 10, 2026

A comprehensive EEG dataset comprising 54 healthy subjects performing three major brain-computer interface (BCI) paradigms—motor imagery, event-related potentials, and steady-state visually evoked potentials—across two sessions. The dataset investigates BCI illiteracy rates and performance variations, revealing that motor imagery exhibits the highest illiteracy rate (53.7%) compared to other paradigms, while all participants demonstrated proficiency with at least one BCI system.

Participants

23

Channels

62 (10-05)

Citations

99

HED

v8.4.0

Size

60.8 GB

Version

v1.0.1

Updated

Jul 10, 2026

BigP3BCI Study R is a P300-based brain-computer interface dataset comprising EEG recordings from 20 ALS subjects performing a 9x8 character grid speller task across two sessions. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset with ~267 subjects across 20 studies. The data were acquired at 256 Hz using 32-channel EEG with a g.USBamp amplifier and are organized in BIDS format with HED event annotations for standardized analysis and machine learning applications.

Participants

20

Channels

32 (10-10)

HED

v8.4.0

Size

1.95 GB

Version

v1.0.1

Updated

Jul 10, 2026