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BigP3BCI Study G is a P300-based brain-computer interface dataset comprising EEG recordings from 20 healthy subjects performing a 9x8 checkerboard visual speller task. This derivative dataset is Study G of 20 studies in the larger BigP3BCI collection, the largest public P300 BCI dataset with ~267 subjects total. The data were acquired at 256 Hz using 16-channel EEG with a g.USBamp amplifier and include target and non-target event classifications suitable for machine learning applications.

Participants

20

Size

327 MB

Version

v1.0.1

Updated

Jun 3, 2026

Imported from OpenNeuro ds003800

Participants

13

Size

364 MB

Version

v1.0.0

Updated

May 23, 2026

BigP3BCI Study D is a P300-based brain-computer interface dataset comprising EEG recordings from 17 healthy subjects performing a 6x6 character grid speller task using dynamic row-column stimulus presentation. 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 using HED 8.4.0 schema. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset, and is designed for machine learning model development and benchmarking in brain-computer interface research.

Participants

17

Size

731 MB

Version

v1.0.1

Updated

Jun 3, 2026

This dataset comprises 64-channel EEG recordings with concurrent ECG and PPG measurements from 30 healthy young adults during resting state and a verbal working memory task. Participants performed digit span recall under four presentation modes (simultaneous, fast sequential, fast sequential with delay, and slow sequential), enabling investigation of neural oscillations and peripheral physiological responses during working memory encoding, maintenance, and retrieval. The dataset includes behavioral accuracy measures and trial-by-trial performance metrics.

Participants

30

Size

41.4 GB

Version

v1.0.0

Updated

Jun 29, 2026

Imported from OpenNeuro ds004745

Participants

6

Citations

26

Size

242 MB

Version

v1.0.0

Updated

May 23, 2026

MIPDB is a multimodal neuroimaging resource comprising high-density EEG and eye-tracking data collected from 111 participants spanning childhood to adulthood. Participants completed a standardized battery of six cognitive and perceptual paradigms (resting state, surround suppression, naturalistic viewing, contrast-change detection, sequence learning, and symbol search) designed to probe information processing maturation across development. This dataset enables investigation of neurodevelopmental trajectories in sensory and cognitive processing.

Size

110 GB

Version

v1.0.0

Updated

Jun 17, 2026

The NeuroTechs Dataset for Stem Skills is an EEG neuroimaging dataset designed to investigate neural correlates of science, technology, engineering, and mathematics (STEM) skill acquisition and performance. The dataset contains electroencephalographic recordings from participants and is formatted according to the Brain Imaging Data Structure (BIDS) standard, facilitating standardized analysis and reproducibility in cognitive neuroscience research.

Participants

63

Size

1.39 GB

Version

v1.0.0

Updated

Jun 29, 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

Citations

61

Size

1.22 GB

Version

v1.0.1

Updated

Jun 3, 2026

This dataset comprises intracranial EEG recordings from a single patient undergoing single-pulse electrical stimulation, collected at Mayo Clinic as part of an NIH-funded initiative to develop a comprehensive database of stimulation responses. The study aims to advance understanding of human brain connectivity and network responses to electrical stimulation across the lifespan through systematic analysis of evoked potentials and network effects.

Participants

1

Citations

2

Size

620 MB

Version

v1.0.0

Updated

Jun 22, 2026

This dataset contains intracranial electrophysiology recordings and behavioral data from a delayed free recall task with real-time closed-loop brain stimulation during encoding. Participants studied word lists, performed distractor arithmetic tasks, and freely recalled words while an L2 logistic regression classifier trained on encoding neural features guided stimulation delivery to optimize memory performance. Data were collected across multiple clinical sites as part of a collaborative memory research initiative.

Participants

18

Size

34.7 GB

Version

v1.0.0

Updated

Jun 27, 2026