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nm000341
Cattan, Rodrigues & Congedo 2019 — Passive Head-Mounted Display Music-Listening EEG dataset (PHMD)

EEG

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 16-channel EEG system sampling at 512 Hz. A passive head-mounted display is a head-mounted device without integrated electronics, consisting of a smartphone holder. The dataset provides raw, unfiltered EEG data in mat and csv formats, designed to support research on the effects of head-mounted displays and auditory stimuli on brain activity. This is a BIDS-converted derivative of the original Zenodo dataset (10.5281/zenodo.2617084).

Participants

12

Channels

16 (10-10)

Citations

19

HED

v8.4.0

Size

516 MB

Version

v1.0.5

Updated

Aug 18, 2026

by G. Cattan et al.
nm000340
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study J)

EEG

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 g.USBamp amplifier and include target and non-target event classifications suitable for machine learning applications.

Participants

20

Channels

16 (10-10)

HED

v8.4.0

Size

1.49 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Boyla Mainsah et al.
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.
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.
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.
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.
nm000277
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study G)

EEG

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 one 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

Channels

16 (10-10)

HED

v8.4.0

Size

1.62 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Boyla Mainsah et al.
nm000272
Romani et al. 2025 — BrainForm: a Serious Game for BCI Training and Data Collection (P300 ERP, University of Trento)

EEG

This dataset comprises scalp EEG recordings from 22 participants who used BrainForm, a gamified brain-computer interface (BCI) designed for P300 event-related potential (ERP) training and scalable data collection with consumer hardware. Participants performed repeated runs of a P300 spelling/selection task, allowing analysis of BCI skill acquisition across sessions and the effects of different visual stimulation textures on performance. The dataset supports research on BCI training, human factors, and machine learning applications on ERP data.

Participants

22

Channels

8 (10-10)

Citations

1

HED

v8.4.0

Size

237 MB

Version

v1.0.6

Updated

Aug 20, 2026

by Michele Romani et al.