Discover datasets

A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing motor imagery and motor execution tasks. The dataset comprises 2,520 trials across 63 sessions with 64-channel EEG recordings at 1000 Hz sampling rate, focusing on left and right hand grasping imagery. This resource supports the development and benchmarking of brain-computer interface (BCI) systems for motor control applications.

Participants

31

Citations

8

HED

v8.4.0

Size

4.65 GB

Version

vv1.0.0

Updated

Jul 10, 2026

The BNCI 2015-009 AMUSE dataset comprises EEG recordings from 21 healthy subjects performing an auditory oddball task using spatial hearing as a discriminating cue. The dataset implements a P300-based brain-computer interface paradigm with multi-class auditory stimuli presented from five spatially distributed speakers at varying inter-stimulus intervals. Preprocessed data includes 60 EEG channels and 2 EOG channels sampled at 100 Hz (downsampled from 250 Hz acquisition rate), with offline classification achieving up to 100% accuracy on best-performing individual subjects.

Participants

21

Citations

159

HED

v8.4.0

Size

4.57 GB

Version

vv1.0.0

Updated

Jul 10, 2026

A P300-based brain-computer interface dataset comprising EEG recordings from 8 subjects (4 disabled, 4 able-bodied) performing a visual oddball task with image stimuli. The dataset includes 32-channel EEG data sampled at 2048 Hz from 4 sessions per subject, with 810 total trials per subject (135 targets, 675 non-targets) classified using Fisher's linear discriminant analysis and Bayesian linear discriminant analysis. This derivative dataset demonstrates high classification accuracy (100%) and information transfer rates suitable for BCI applications in disabled populations.

Participants

8

Citations

82

HED

v8.4.0

Size

1.94 GB

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 motor imagery EEG dataset comprising 31 healthy participants performing left and right hand grasping imagery tasks across multiple sessions. The dataset includes 2,520 trials recorded at 1000 Hz using a 64-channel Neuroscan system with standardized electrode montage, designed for brain-computer interface research and motor control applications.

Participants

31

Citations

8

HED

v8.4.0

Size

4.71 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