Discover datasets

A P300-based brain-computer interface dataset for home appliance control, comprising EEG recordings from 15 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 for electric light control applications.

Participants

15

Size

628 MB

Version

v1.0.0

Updated

Jun 3, 2026

This dataset comprises EEG recordings from 10 healthy subjects performing a P300-based brain-computer interface task for home appliance control. The experiment employed a visual oddball paradigm with 25-channel EEG acquisition at 500 Hz, featuring target and non-target stimulus classifications (2 event classes). The dataset includes 50 training and 30 testing trials per subject, with visual feedback provided during online BCI operation.

Participants

10

Size

413 MB

Version

v1.0.0

Updated

Jun 3, 2026

# Dataset Description ## Overview This dataset contains EEG recordings from a study investigating neural activity changes before and after an intervention. The data are organized following the Brain Imaging Data Structure (BIDS) specification. The dataset includes multiple participant groups and timepoints: - Group 1, Group 2, Group 3 - Pre-intervention (pre) and Post-intervention (post) ## Participants Participants are labeled using anonymized IDs (e.g., sub-001, sub-002, etc.). Demographic

Participants

67

Size

686 MB

Updated

Jun 7, 2026

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

Size

81.5 MB

Version

v1.0.0

Updated

Jun 3, 2026

A gaze-independent brain-computer interface dataset based on covert spatial attention shifts for binary communication. The dataset comprises EEG recordings from 18 healthy participants performing a visual attention task with N2pc event-related potential detection. Participants responded to yes/no questions by covertly shifting attention to colored visual stimuli while maintaining central gaze fixation. The original study reported 88.5% mean classification accuracy using canonical correlation analysis.

Participants

18

Size

1023 MB

Version

v1.0.0

Updated

Jun 3, 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 collected at 256 Hz sampling rate using 16 channels in a standard 10-20 montage and represent a subset of the BigP3BCI study, the largest public P300 BCI dataset. This derivative dataset is processed and annotated according to BIDS standards with HED 8.4.0 event annotations for use in BCI research and benchmarking.

Participants

16

Size

321 MB

Version

v1.0.0

Updated

Jun 3, 2026

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

Size

4.32 GB

Version

v1.0.0

Updated

Jun 3, 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

Size

1.93 GB

Version

v1.0.0

Updated

Jun 3, 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

Size

401 MB

Version

v1.0.0

Updated

Jun 3, 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

Size

1.26 GB

Version

v1.0.0

Updated

Jun 3, 2026