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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

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

Size

1.22 GB

Version

v1.0.1

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

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

Size

371 GB

Version

v1.0.1

Updated

Jun 3, 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

Size

60.8 GB

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

# Delineating neural contributions to EEG-based speech decoding ## Overview 128-channel EEG recordings during speech production tasks. Participants produced one of 5 color words (green, magenta, orange, violet, yellow) under three speech conditions: overt, minimally overt, and covert. Each trial consists of 5 repetitions of the same word (1.25 sec per repetition). ## Channel layout (139 channels total) - Channels 1-128: EEG - Channels 129-130: DISPLAY (bipolar pair, misc) - Channels 131-132:

Participants

3

Size

1.62 GB

Updated

Jun 6, 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