Discover datasets

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

Citations

64

HED

v8.4.0

Size

1.26 GB

Version

v1.0.0

Updated

Jul 10, 2026

A P300-based brain-computer interface dataset for television control comprising EEG recordings from 30 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 (2 classes), designed to evaluate BCI performance for home appliance control applications.

Participants

30

Citations

8

HED

v8.4.0

Size

1.40 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 16 healthy participants performing motion visually evoked potential (mVEP) speller tasks using three different paradigm variants: overt gaze-directed, covert attention-based, and foveal motion center stimulation. The study investigates gaze-independent brain-computer interface communication through P300-based visual speller applications with two-level hierarchical selection from 6 groups/symbols per level, including copy-spelling and free-spelling conditions.

Participants

16

Citations

44

HED

v8.4.0

Size

1.27 GB

Version

v1.0.0

Updated

Jul 10, 2026

Participants

15

Citations

2

HED

v8.4.0

Size

8.65 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 15 individuals with autism spectrum disorder (ASD) participating in a P300-based brain-computer interface (BCI) study focused on joint-attention training in a virtual environment. Data were collected across 7 sessions using 8 EEG channels sampled at 250 Hz, with participants responding to visual stimuli (flashing objects) in a two-class paradigm (target vs. non-target). The dataset includes preprocessed, epoched data suitable for benchmarking BCI classification algorithms and investigating neural correlates of attention in ASD populations.

Participants

15

Citations

66

HED

v8.4.0

Size

3.79 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 25 healthy participants performing motor imagery and spatial attention tasks across five sessions. The study investigates the integration of simultaneous motor imagery and overt spatial attention for brain-computer interface control, with participants performing left-hand and right-hand imagery tasks while receiving visual cursor feedback. Data were acquired using a 64-channel Neuroscan system at 1000 Hz sampling rate, yielding 1875 trials suitable for BCI classifier development and evaluation.

Participants

25

Citations

21

HED

v8.4.0

Size

4.89 GB

Version

v1.0.1

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 15 healthy subjects performing a P300-based brain-computer interface task for door lock control. The experiment employed a visual oddball paradigm with 31-channel EEG acquisition at 500 Hz, generating event-related potentials in response to target and non-target stimuli presented on an LCD display. The dataset includes 50 training and 30 testing trials per subject and serves as a benchmark for P300-based home appliance control applications.

Participants

14

Citations

8

HED

v8.4.0

Size

606 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 15 healthy subjects performing an auditory brain-computer interface task based on the ASME (Auditory Stream segregation Multiclass ERP) paradigm. The study investigates two strategies for achieving four-class classification: ASME-4stream (four independent streams with single target stimuli) and ASME-2stream (two streams with dual target stimuli each). EEG data were recorded at 1000 Hz from 64 channels and analyzed using event-related potential methods and linear discriminant analysis classification, achieving accuracies of 83% and 86% respectively.

Participants

15

Channels

64 (10-10)

Citations

4

HED

v8.4.0

Size

13.9 GB

Version

v1.0.1

Updated

Jul 10, 2026

A multi-session EEG dataset acquired from 15 healthy participants performing resting state and graded cognitive tasks (MATB-II at three difficulty levels). The dataset comprises 62-channel EEG recordings at 500 Hz sampling rate designed for passive brain-computer interface applications and mental workload estimation in neuroergonomic contexts. Raw EEG data are provided with standardized event annotations using HED 8.4.0 schema and MOABB-compatible feature extraction pipelines (bandpower analysis and Riemannian covariance methods) for benchmarking purposes.

Participants

15

Citations

56

HED

v8.4.0

Size

1.22 GB

Version

v1.0.0

Updated

Jul 10, 2026

A collaborative brain-computer interface dataset based on rapid serial visual presentation (RSVP) paradigm, comprising synchronized EEG recordings from 14 healthy subjects (7 pairs) performing target detection tasks on human versus non-human images. The dataset includes 62-channel EEG data sampled at 1000 Hz across 2 sessions per subject, with event-related potential (ERP) markers for target and non-target stimuli, designed to support BCI research and benchmarking studies.

Participants

14

Citations

26

HED

v8.4.0

Size

5.29 GB

Version

v1.0.0

Updated

Jul 10, 2026