Discover datasets

BigP3BCI Study B is a derivative EEG dataset containing P300 brain-computer interface recordings from 19 ALS patients across 8 sessions each, using a 6x6 checkerboard visual speller paradigm. The dataset comprises 16-channel EEG data sampled at 256 Hz, annotated with target and non-target event classifications using HED 8.4.0 schema. This dataset is a subset of the larger BigP3BCI collection, the largest public P300 BCI dataset with recordings from approximately 267 subjects across multiple studies.

Participants

19

Channels

16 (10-10)

HED

v8.4.0

Size

4.28 GB

Version

v1.0.2

Updated

Aug 18, 2026

…Assessing mismatch negativity (MMN) and P3b within‐individual sensitivity — A comparison between…

Participants

15

Channels

64 (10-10)

Citations

5

Size

16.5 GB

Version

v1.0.0

Updated

Aug 19, 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. 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

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

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

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

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

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

BigP3BCI Study A is a derivative P300 brain-computer interface dataset containing 32-channel EEG recordings from 13 healthy subjects performing a visual speller task using a 6×6 checkerboard stimulus paradigm. The dataset comprises EEG data sampled at 256 Hz with binary classification of target and non-target events, annotated using HED 8.4.0 schema and processed through the MOABB framework.

Participants

13

Channels

32 (10-10)

HED

v8.4.0

Size

3.86 GB

Version

v1.0.3

Updated

Aug 18, 2026

BigP3BCI Study P is a P300-based brain-computer interface dataset comprising EEG recordings from 19 healthy subjects across 2 sessions each, using a 9x8 character grid spelling paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz with target and non-target visual stimuli annotated 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 BCI research.

Participants

19

Channels

32 (10-10)

HED

v8.4.0

Size

4.96 GB

Version

v1.0.3

Updated

Aug 18, 2026