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BigP3BCI Study P is a P300-based brain-computer interface dataset comprising EEG recordings from 19 healthy subjects across 2 sessions each, using a 9x8 character grid spelling paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 montage, annotated with target and non-target visual stimulus events. This derivative dataset is part of the largest public P300 BCI dataset collection, and is optimized for machine learning applications in BCI research.

Participants

19

Size

1.46 GB

Version

v1.0.1

Updated

Jun 3, 2026

A multi-day, high-quality EEG dataset for motor imagery brain-computer interface research comprising 51 healthy subjects performing left and right hand motor imagery tasks across three sessions, with an additional 11 subjects performing 3-class motor imagery (left hand, right hand, foot). The dataset includes 59 EEG channels sampled at 1000 Hz with standardized 10-05 electrode montage, totaling 39,600 trials (51 subjects × 3 sessions × 200 trials for 2-class + 11 subjects × 3 sessions × 300 trials for 3-class) with visual and auditory cues. This resource is designed to support the development and benchmarking of motor imagery BCI algorithms and classifiers.

Participants

51

Size

63.4 GB

Version

v1.0.1

Updated

Jun 3, 2026

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

Size

148 MB

Version

v1.0.0

Updated

Jun 3, 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

Size

142 MB

Version

v1.0.0

Updated

Jun 3, 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

Size

148 MB

Version

v1.0.0

Updated

Jun 3, 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

Size

143 MB

Version

v1.0.0

Updated

Jun 3, 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

Size

426 MB

Version

v1.0.1

Updated

Jun 3, 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

Size

1.95 GB

Version

v1.0.1

Updated

Jun 3, 2026

BigP3BCI Study C is a P300-based brain-computer interface dataset comprising EEG recordings from 19 healthy subjects performing a visual speller task using a 6x6 checkerboard paradigm. The dataset contains single-session recordings with 32-channel EEG data sampled at 256 Hz, annotated with target and non-target event labels. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset, and is formatted according to BIDS standards with HED event annotations for machine learning applications.

Participants

19

Size

1.22 GB

Version

v1.0.1

Updated

Jun 3, 2026

BigP3BCI Study Q is a P300-based brain-computer interface dataset comprising EEG recordings from 36 ALS subjects across 3 sessions each, using a 6x6 color intensification speller paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 electrode montage, annotated with target and non-target event labels for machine learning applications. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset with recordings from approximately 267 subjects across 20 studies.

Participants

36

Size

1.09 GB

Version

v1.0.1

Updated

Jun 3, 2026