Discover datasets

The Alphawaves dataset comprises resting-state EEG recordings from 19 healthy participants acquired using a 16-channel g.tec g.USBamp system at 512 Hz sampling rate. Participants alternated between eyes-closed and eyes-open conditions in 10-second trials, with event-related synchronization analysis focused on alpha frequency band activity (8-12 Hz). This dataset serves as a benchmark resource for brain-computer interface research and resting-state EEG analysis.

Participants

19

Channels

16 (10-10)

HED

v8.4.0

Size

178 MB

Version

v1.0.3

Updated

Aug 18, 2026

A gaze-independent brain-computer interface dataset based on covert spatial attention shifts for binary communication. This EEG dataset comprises recordings from 18 healthy participants performing a visual attention task with N2pc event-related potential detection. The paradigm uses colored visual stimuli (green crosses for 'yes', red crosses for 'no') presented while maintaining central gaze fixation, achieving 88.5% mean accuracy with canonical correlation analysis classification.

Participants

18

Channels

30 (10-10)

Citations

26

HED

v8.4.0

Size

1.82 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 16 healthy subjects performing a P300-based brain-computer interface task using a 9x8 checkerboard stimulus grid with gaze conditions. The data were acquired at 256 Hz using 16 channels in a standard 10-20 montage and include target and non-target event classifications for P300 detection and speller applications. This derivative dataset is part of the BigP3BCI project, the largest public P300 BCI dataset containing recordings from approximately 267 subjects across multiple studies.

Participants

16

Channels

16 (10-10)

HED

v8.4.0

Size

1.76 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset (BI2015b) comprises EEG recordings from 44 subjects playing a visual P300-based Brain-Computer Interface videogame called Brain Invaders, collected at GIPSA-lab, Grenoble, France in 2015. Using an oddball paradigm on a 36-symbol grid, participants elicited P300 responses under three flash-duration conditions (50ms, 80ms, 110ms) across three game sessions. The dataset was reformatted into BIDS by MOABB and includes HED event annotations for Target and NonTarget stimuli, intended for benchmarking BCI decoding algorithms.

Participants

44

Channels

32 (10-10)

Citations

9

HED

v8.4.0

Size

8.59 GB

Version

v1.0.4

Updated

Aug 21, 2026

This dataset comprises electroencephalographic recordings from 43 healthy subjects performing a visual P300-based brain-computer interface task using the Brain Invaders videogame paradigm. The experiment took place at GIPSA-lab, Grenoble, France, in 2015. Participants responded to an oddball stimulus presentation (36-symbol grid with 1 target, 35 non-targets) across three sessions with varying flash durations (50ms, 80ms, 110ms). EEG data were acquired using 32 wet electrodes at 512 Hz and processed with a calibration-free adaptive Riemannian classifier for real-time feedback. Resting state recordings (eyes closed, approximately one minute) were acquired before and after each session.

Participants

43

Channels

32 (10-10)

Citations

39

HED

v8.4.0

Size

6.19 GB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset comprises electroencephalographic recordings from 38 healthy subjects engaged in a multi-user P300-based Brain-Computer Interface experiment called Brain Invaders. Participants played in pairs across three conditions (Solo1, Solo2, and Collaboration) using a visual oddball paradigm with 36 symbols to elicit P300 responses. The study investigates inter-brain synchrony during collaborative BCI gaming, with 32-channel EEG recordings sampled at 512 Hz using wet electrodes in a standard 10-10 montage.

Participants

38

Channels

32 (10-10)

Citations

15

HED

v8.4.0

Size

1.24 GB

Version

v1.0.2

Updated

Aug 18, 2026

A code-modulated visual evoked potential (cVEP) brain-computer interface dataset from 30 healthy participants performing an offline BCI task with a calculator grid stimulus. This derivative dataset demonstrates a calibration-free approach that progressively reduces training data requirements, ultimately achieving zero-training performance through neural encoding models while maintaining classification accuracy comparable to traditional event-related potential methods.

Participants

30

Channels

8 (10-10)

Citations

66

HED

v8.4.0

Size

3.16 GB

Version

v1.0.4

Updated

Aug 18, 2026

A P300-based brain-computer interface dataset for television control comprising EEG recordings from 30 healthy subjects performing a visual oddball task. The dataset includes 31-channel EEG data acquired at 500 Hz using active electrodes in a standard 10-20 montage, with target and non-target stimulus classifications for home appliance control applications. Data were collected in an online feedback paradigm with visual flash stimuli presented at 750 ms stimulus onset asynchrony.

Participants

30

Channels

31 (10-10)

Citations

8

HED

v8.4.0

Size

3.09 GB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 16 healthy participants performing motion visually evoked potential (mVEP) speller tasks using three different paradigm variants: overt gaze-directed, covert attention-based, and foveal motion center stimulation. The study investigates gaze-independent brain-computer interface communication through motion VEP-based P300 visual speller applications with two-level hierarchical selection from 30 symbols (6 symbols per level), employing 63-channel EEG recordings at 100 Hz sampling rate with preprocessed data including artifact rejection and baseline correction.

Participants

16

Channels

63 (10-10)

Citations

44

HED

v8.4.0

Size

3.79 GB

Version

v1.0.2

Updated

Aug 18, 2026

A longitudinal EEG dataset comprising event-related potentials (ERPs) from 15 healthy participants performing a rapid serial visual presentation (RSVP) face authentication task. The dataset includes 4 sessions per subject collected over 200 days, with 57-channel EEG recordings at 1000 Hz sampling rate. Participants discriminated between self-face and AI-generated faces in a p300-based identity authentication paradigm, yielding approximately 160 target and 6240 non-target trials per session. This derivative dataset was generated from raw EEG data using MOABB 1.5.0 and is organized in BIDS format.

Participants

15

Channels

57 (10-10)

Citations

3

HED

v8.4.0

Size

57.0 GB

Version

v1.0.2

Updated

Aug 18, 2026