Discover datasets

This dataset comprises EEG recordings from 16 healthy participants performing a code-modulated visual evoked potential (c-VEP) brain-computer interface task using p-ary m-sequences. The study evaluates non-binary m-sequence stimulation patterns to enhance user comfort in c-VEP-based BCIs. Data were collected across 5 sessions per subject with 8 runs per session at 256 Hz sampling rate using 16 EEG channels, providing a comprehensive resource for BCI research and algorithm development.

Participants

16

Citations

28

HED

v8.4.0

Size

764 MB

Version

v1.0.0

Updated

Jul 10, 2026

A 7-day longitudinal motor imagery brain-computer interface (BCI) EEG dataset comprising 20 healthy participants performing four imagery tasks (left hand, right hand, both feet, and rest) across 7 sessions. The dataset contains 33,600 trials recorded at 500 Hz from 41 EEG channels using a Neuroscan SynAmps2 system with visual arrow cue stimulation. Data were preprocessed and annotated with HED 8.4.0 tags, supporting BCI research and algorithm development for motor imagery decoding.

Participants

20

Citations

15

HED

v8.4.0

Size

16.0 GB

Version

v1.0.0

Updated

Jul 10, 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

Citations

5

HED

v8.4.0

Size

358 MB

Version

v1.0.0

Updated

Jul 10, 2026

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

11

HED

v8.4.0

Size

4.65 GB

Version

v1.0.0

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

v1.0.0

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

v1.0.0

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

3

HED

v8.4.0

Size

5.75 GB

Version

v1.0.0

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

11

HED

v8.4.0

Size

4.71 GB

Version

v1.0.0

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

v1.0.0

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

v1.0.0

Updated

Jul 10, 2026