Discover datasets

This dataset contains EEG recordings from a collaborative brain-computer interface (BCI) study using rapid serial visual presentation (RSVP) for target image detection. Fourteen healthy subjects, organized into seven pairs, performed synchronized RSVP tasks distinguishing human from non-human images, generating p300-like event-related potentials. The dataset is a BIDS-formatted derivative converted using the MOABB toolbox from the original data by Zheng et al. (2020).

Participants

14

Channels

62 (10-10)

Citations

27

HED

v8.4.0

Size

11.9 GB

Version

v1.0.3

Updated

Aug 21, 2026

A P300-based brain-computer interface dataset for home appliance control, comprising EEG recordings from 14 healthy subjects performing a visual oddball task with 31-channel recordings at 500 Hz sampling rate. The dataset includes 50 training and 30 testing blocks per subject, with target and non-target stimulus classifications presented via LCD display in an online BCI paradigm.

Participants

14

Channels

31 (10-10)

Citations

9

HED

v8.4.0

Size

644 MB

Version

v1.0.3

Updated

Aug 18, 2026

Participants

13

Citations

2

HED

v8.4.0

Size

2.82 GB

Version

v1.0.0

Updated

Jul 10, 2026

Participants

25

Citations

3

HED

v8.4.0

Size

54.4 MB

Version

v1.0.0

Updated

Jul 10, 2026

This derivative dataset presents an event-related potential (ERP) paradigm from the Mobile BCI dataset, comprising 24 healthy participants performing visual oddball tasks during various locomotion states (standing, walking, running). The dataset includes 73-channel EEG recordings sampled at 100 Hz with 5 sessions per subject, annotated with HED 8.4.0 event tags for target and non-target stimuli. This resource enables investigation of P300-based brain-computer interface performance in mobile and dynamic environments.

Participants

24

Channels

48 (10-10)

Citations

34

HED

v8.4.0

Size

8.08 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 13 healthy subjects performing a P300-based brain-computer interface task using a 9x8 checkerboard stimulus paradigm. Participants completed a single session of visual perception tasks designed for speller applications, with 16-channel EEG data sampled at 256 Hz. This derivative dataset is part of the BigP3BCI study, the largest public P300 BCI dataset containing recordings from approximately 267 subjects across multiple studies.

Participants

13

Channels

16 (10-10)

HED

v8.4.0

Size

1.57 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises event-related potential (ERP) recordings from 13 healthy subjects performing a visual matrix speller task using a calibrationless brain-computer interface approach. The study introduces learning from label proportions (LLP), an unsupervised classification method that exploits known target/non-target stimulus ratios to enable online BCI operation without prior calibration. Subjects performed copy-spelling tasks using a 6×7 character grid across three sessions, achieving 84.5% character accuracy without labeled training data.

Participants

13

Channels

31 (10-10)

Citations

46

HED

v8.4.0

Size

5.15 GB

Version

v1.0.2

Updated

Aug 18, 2026

A P300-based brain-computer interface dataset comprising EEG recordings from 13 healthy participants performing a visual speller task using covert spatial attention and feature attention mechanisms. The dataset includes data from three speller variants (Hex-o-Spell, Cake Speller, and Center Speller) with 63-channel recordings at 250 Hz sampling rate. Preprocessed data includes downsampling, filtering, and baseline correction. The original study reported 97% accuracy on the Center Speller variant.

Participants

13

Channels

63 (10-05)

Citations

81

HED

v8.4.0

Size

9.27 GB

Version

v1.0.2

Updated

Aug 18, 2026

This derivative dataset comprises EEG recordings from 21 ALS subjects participating in the BigP3BCI Study M, a P300-based brain-computer interface experiment using a 9x8 adaptive/checkerboard visual speller paradigm. The data were acquired at 256 Hz using 16-channel EEG montage and represent a subset of the largest public P300 BCI dataset containing recordings from approximately 267 subjects across 20 studies. This dataset is processed and organized according to BIDS standards for facilitating reproducible BCI research and benchmarking.

Participants

21

Channels

16 (10-10)

HED

v8.4.0

Size

2.38 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises preprocessed EEG recordings from 12 healthy participants performing a brain-computer interface task based on broad-band visually evoked potentials (BBVEPs). Participants controlled a 6×6 matrix speller by attending to target symbols while cells were stimulated with pseudo-random Gold code sequences. The study demonstrates a novel reconvolution approach for template generation that achieves 86% online accuracy and an information transfer rate of 48 bits per minute.

Participants

12

Channels

64 (10-10)

Citations

72

HED

v8.4.0

Size

11.0 GB

Version

v1.0.3

Updated

Aug 18, 2026