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nm000336
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study R)

EEG

BigP3BCI Study R is a P300-based brain-computer interface dataset comprising EEG recordings from 20 subjects performing a 9x8 multi-face 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 formatted according to BIDS standards with HED event annotations for standardized analysis and machine learning applications.

Participants

20

Channels

32 (10-10)

HED

v8.4.0

Size

6.59 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Boyla Mainsah et al.
nm000329
Brandl et al. 2020 — Motor Imagery Under Distraction: An Open Access BCI Dataset

EEG

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 recorded at 1000 Hz with 63 EEG channels, including one calibration run without feedback and six feedback runs with distinct distraction manipulations.

Participants

16

Channels

63 (10-05)

Citations

13

HED

v8.4.0

Size

72.1 GB

Version

v1.0.7

Updated

Aug 18, 2026

by Stephanie Brandl et al.
nm000326
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study C)

EEG

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

4.11 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Boyla Mainsah et al.
nm000323
Lee et al. 2019 (ERP) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy

EEG

This dataset comprises EEG recordings from 54 healthy participants performing a P300-based brain-computer interface speller task, designed to investigate BCI illiteracy. The study includes 62 EEG channels and 4 EMG channels sampled at 1000 Hz, with participants completing offline training and online test phases using a 36-symbol row-column speller paradigm. The dataset achieved 96.7% accuracy with an 11.1% BCI illiteracy rate, providing a comprehensive resource for evaluating P300-based BCI performance and individual differences in BCI competence.

Participants

54

Channels

62 (10-05)

Citations

100

HED

v8.4.0

Size

132 GB

Version

v1.0.4

Updated

Aug 18, 2026

by Min-Ho Lee et al.
nm000321
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study Q)

EEG

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 using HED 8.4.0 schema. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset, and is optimized for machine learning applications in brain-computer interface research.

Participants

36

Channels

32 (10-10)

HED

v8.4.0

Size

3.70 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Boyla Mainsah et al.
nm000313
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study S2)

EEG

BigP3BCI Study S2 is a P300-based brain-computer interface dataset comprising EEG recordings from 24 healthy subjects performing a 9x8 house/tool visual speller paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz, annotated with target and non-target event classifications using HED 8.4.0 schema. This dataset is part of the larger BigP3BCI collection (the largest public P300 BCI dataset with recordings from approximately 267 subjects across 20 studies), designed to support machine learning research and BCI benchmarking. Trial intervals span 0-1.0 seconds from stimulus onset.

Participants

24

Channels

32 (10-10)

HED

v8.4.0

Size

3.74 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Boyla Mainsah et al.
nm000311
Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)

EEG

A multimodal EEG dataset comprising motor imagery and motor execution data from 25 healthy subjects performing 11 intuitive upper-limb movement tasks (6 reaching, 3 grasping, 2 wrist twisting) across 3 sessions. The dataset includes 71-channel EEG recordings (60 EEG, 4 EOG, 7 EMG channels) sampled at 1000 Hz with synchronized behavioral annotations, designed for brain-computer interface research and motor control applications. This is a BIDS-formatted derivative dataset converted from the original Jeong et al. 2020 publication using MOABB (Mother of All BCI Benchmarks).

Participants

25

Channels

71 (10-10)

Citations

82

HED

v8.4.0

Size

326 GB

Version

v1.0.2

Updated

Aug 18, 2026

by Ji-Hoon Jeong et al.
nm000310
Guttmann-Flury et al. 2025 (SSVEP) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms

EEG

A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing a 4-class steady-state visually evoked potential (SSVEP) brain-computer interface task. The dataset comprises 3,024 trials across 63 sessions with 66-channel recordings (64 EEG + 1 EOG + 1 stim) at 1000 Hz sampling rate, designed to investigate ocular activity patterns and their relationship to BCI performance across multiple paradigms.

Participants

11

Channels

62 (10-10)

Citations

12

HED

v8.4.0

Size

28.6 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Eva Guttmann-Flury et al.
nm000303
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study O)

EEG

BigP3BCI Study O is a P300-based brain-computer interface dataset comprising EEG recordings from 18 ALS subjects across 2 sessions each, using a 9x8 character grid with supervised and checkerboard stimulus paradigms. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 electrode montage, designed for machine learning applications in BCI speller systems. 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

18

Channels

32 (10-10)

HED

v8.4.0

Size

3.23 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Boyla Mainsah et al.
nm000301
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study D)

EEG

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. The dataset contains single-session recordings acquired at 256 Hz using 32-channel EEG with standard 10-20 montage, 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

17

Channels

32 (10-10)

HED

v8.4.0

Size

2.41 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Boyla Mainsah et al.