Discover datasets

A multi-day motor imagery brain-computer interface (MI-BCI) dataset comprising EEG recordings from 51 healthy subjects performing left and right hand motor imagery tasks across three sessions. The dataset includes 59-channel EEG data (plus 1 ECG and 4 EOG channels) sampled at 1000 Hz with standardized 10-05 electrode montage, totaling 39,600 trials with visual and auditory cues. This resource supports the development and benchmarking of BCI algorithms for motor control applications.

Participants

51

Size

58.4 GB

Version

v1.0.0

Updated

Jun 3, 2026

A motor imagery brain-computer interface dataset comprising EEG recordings from 52 healthy subjects performing kinesthetic imagery of left and right hand finger movements. The dataset includes 100-120 trials per class with concurrent EMG recordings, psychological and physiological questionnaires, 3D electrode coordinates, and non-task-related control states. Mean classification accuracy of 67.46% (±13.17%) demonstrates substantial performance variation across subjects, making it valuable for investigating subject-to-subject transfer in BCI applications.

Participants

52

Size

6.75 GB

Version

v1.0.0

Updated

Jun 3, 2026

This dataset comprises electroencephalographic (EEG) recordings from 71 healthy subjects performing a visual P300-based brain-computer interface task using the Brain Invaders videogame. Participants engaged with an oddball paradigm involving pseudo-random flashes of 36 symbols on a grid to elicit P300 event-related potentials. The study evaluated the feasibility of a calibration-less P300 BCI system using 16 dry electrodes, with real-time adaptive classification for online feedback during gameplay. Subjects completed up to three game sessions.

Participants

64

Size

1.03 GB

Version

v1.0.0

Updated

Jun 3, 2026

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

Size

358 MB

Version

v1.0.0

Updated

Jun 3, 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

Size

5.75 GB

Version

v1.0.0

Updated

Jun 3, 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

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

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