Discover datasets

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

59

HED

v8.4.0

Size

1.22 GB

Version

vv1.0.1

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

21

HED

v8.4.0

Size

143 MB

Version

vv1.0.0

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

vv1.0.1

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

vv1.0.1

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

57

HED

v8.4.0

Size

371 GB

Version

vv1.0.1

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

98

HED

v8.4.0

Size

60.8 GB

Version

vv1.0.1

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

vv1.0.1

Updated

Jul 10, 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

Channels

63 (10-05)

Citations

12

HED

v8.4.0

Size

61.6 GB

Version

vv1.0.1

Updated

Jul 10, 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

Channels

32 (10-10)

HED

v8.4.0

Size

1.22 GB

Version

vv1.0.1

Updated

Jul 10, 2026

This EEG dataset comprises recordings from 54 healthy participants performing a P300-based copy-spelling task using a 36-symbol row-column speller paradigm. The dataset includes 62 EEG channels and 4 EMG channels sampled at 1000 Hz, with offline training and online test phases designed to investigate BCI illiteracy. Data are provided in BIDS format with HED event annotations and represent a benchmark resource for evaluating brain-computer interface performance across different user populations.

Participants

54

Channels

62 (10-05)

Citations

100

HED

v8.4.0

Size

38.6 GB

Version

vv1.0.1

Updated

Jul 10, 2026