Discover datasets

This dataset comprises electroencephalographic recordings from 50 healthy subjects performing a visual P300 brain-computer interface videogame called Brain Invaders. The experiment employed an oddball paradigm with 36 symbols flashed pseudo-randomly across three sessions with varying flash durations (50ms, 80ms, 110ms) to elicit P300 responses. EEG signals were acquired using 32 active wet electrodes at 512 Hz sampling rate, with real-time adaptive classification enabling calibration-free operation of the classifier.

Participants

44

Citations

9

HED

v8.4.0

Size

4.32 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalographic recordings from 43 healthy participants performing a visual P300-based brain-computer interface task using the Brain Invaders videogame paradigm. Participants completed three sessions with varying flash durations (50ms, 80ms, 110ms) while attempting to identify target symbols among 36 pseudo-randomly flashed stimuli. EEG data were acquired using 32 wet electrodes at 512 Hz, with real-time adaptive Riemannian classification providing online feedback in a calibration-free BCI system. This is a derivative dataset processed from raw EEG recordings.

Participants

43

Citations

36

HED

v8.4.0

Size

1.93 GB

Version

vv1.0.0

Updated

Jul 10, 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

Citations

15

HED

v8.4.0

Size

401 MB

Version

vv1.0.0

Updated

Jul 10, 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 while maintaining classification performance, ultimately achieving zero-training BCI operation through neural encoding models and reconvolution techniques.

Participants

30

Citations

64

HED

v8.4.0

Size

1.26 GB

Version

vv1.0.0

Updated

Jul 10, 2026

A P300-based brain-computer interface dataset for television control comprising EEG recordings from 30 healthy subjects performing a visual oddball task with 31-channel recordings at 500 Hz sampling rate. The dataset includes 50 training and 30 testing trials per subject with target and non-target stimulus classifications (2 classes), designed to evaluate BCI performance for home appliance control applications.

Participants

30

Citations

8

HED

v8.4.0

Size

1.40 GB

Version

vv1.0.0

Updated

Jul 10, 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 P300-based visual speller applications with two-level hierarchical selection from 6 groups/symbols per level, including copy-spelling and free-spelling conditions.

Participants

16

Citations

44

HED

v8.4.0

Size

1.27 GB

Version

vv1.0.0

Updated

Jul 10, 2026

Participants

15

Citations

2

HED

v8.4.0

Size

8.65 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 15 individuals with autism spectrum disorder (ASD) participating in a P300-based brain-computer interface (BCI) study focused on joint-attention training in a virtual environment. Data were collected across 7 sessions using 8 EEG channels sampled at 250 Hz, with participants responding to visual stimuli (flashing objects) in a two-class paradigm (target vs. non-target). The dataset includes preprocessed, epoched data suitable for benchmarking BCI classification algorithms and investigating neural correlates of attention in ASD populations.

Participants

15

Citations

66

HED

v8.4.0

Size

3.79 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 25 healthy participants performing motor imagery and spatial attention tasks across five sessions. The study investigates the integration of simultaneous motor imagery and overt spatial attention for brain-computer interface control, with participants performing left-hand and right-hand imagery tasks while receiving visual cursor feedback. Data were acquired using a 64-channel Neuroscan system at 1000 Hz sampling rate, yielding 1875 trials suitable for BCI classifier development and evaluation.

Participants

25

Citations

21

HED

v8.4.0

Size

4.89 GB

Version

vv1.0.1

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 15 healthy subjects performing a P300-based brain-computer interface task for door lock control. The experiment employed a visual oddball paradigm with 31-channel EEG acquisition at 500 Hz, generating event-related potentials in response to target and non-target stimuli presented on an LCD display. The dataset includes 50 training and 30 testing trials per subject and serves as a benchmark for P300-based home appliance control applications.

Participants

14

Citations

8

HED

v8.4.0

Size

606 MB

Version

vv1.0.0

Updated

Jul 10, 2026