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This dataset comprises electroencephalographic recordings from 21 healthy subjects performing a visual P300 experiment in two conditions: personal computer and virtual reality environments. The study compares P300-based brain-computer interface performance across these modalities using a 6×6 matrix speller paradigm with 16-channel EEG acquisition at 512 Hz. The dataset includes 12 experimental blocks per session with randomized session order and stimulus presentation to evaluate physiological responses, subjective experience, and BCI performance differences between PC and VR presentation.

Participants

21

Citations

5

HED

v8.4.0

Size

358 MB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises lower-limb motor imagery EEG recordings from 30 knee pain patients across 5 sessions, with a 2-class paradigm (left vs. right leg imagery). Recorded at 500 Hz using 30 EEG channels with a standard 10-05 montage, the dataset contains 500 trials total (250 per class) designed for brain-computer interface development and rehabilitation applications. The study evaluates multiple classification approaches including CSP+LDA, FBCSP+SVM, EEGNet, and Riemannian geometry methods.

Participants

30

Citations

2

HED

v8.4.0

Size

5.75 GB

Version

vv1.0.0

Updated

Jul 10, 2026

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

Citations

8

HED

v8.4.0

Size

628 MB

Version

vv1.0.0

Updated

Jul 10, 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

Citations

9

HED

v8.4.0

Size

413 MB

Version

vv1.0.0

Updated

Jul 10, 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

HED

v8.4.0

Size

81.5 MB

Version

vv1.0.0

Updated

Jul 10, 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

Citations

26

HED

v8.4.0

Size

1023 MB

Version

vv1.0.0

Updated

Jul 10, 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

HED

v8.4.0

Size

321 MB

Version

vv1.0.0

Updated

Jul 10, 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

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