Discover datasets

This dataset comprises electroencephalographic recordings from 32 participants performing motor imagery tasks during sit-to-stand and stand-to-sit transitions in both offline and online brain-computer interface (BCI) paradigms. Participants completed guided motor imagery trials while seated or standing, with EEG signals recorded from 17 channels at 250 Hz. The dataset includes offline calibration phases used to train machine learning classifiers and corresponding online validation phases where real-time BCI decoding was performed, providing a comprehensive resource for investigating neural correlates of postural transitions and BCI performance.

Participants

32

Channels

17 (10-10)

Size

2.03 GB

Version

v1.0.0

Updated

Jul 10, 2026

The OMEGA Resting State Sample Dataset comprises magnetoencephalography (MEG) recordings from 5 subjects during 5-minute eyes-open resting state sessions acquired at the Montreal Neurological Institute using a CTF 275 system. The dataset includes high-resolution MEG data (297+ channels at 2400 Hz sampling rate), structural T1-weighted MRI images processed with FreeSurfer, 3D head digitization, and fiducial point information organized according to the MEG-BIDS specification. This resource serves as a reference dataset for MEG preprocessing, analysis methodology development, and neuroimaging research.

Participants

6

Size

10.5 GB

Version

v1.0.0

Updated

Jul 10, 2026

A single-subject MEG dataset acquired during an auditory oddball task using a CTF 275 system at 2400 Hz sampling rate. The dataset includes two 6-minute runs with 200 standard tones (440 Hz) and 40 deviant tones (554.4 Hz) presented binaurally, along with behavioral responses, empty room recordings, anatomical MRI, and detailed head digitization. This dataset serves as a tutorial example for the Brainstorm neuroimaging software and is widely used for MEG analysis demonstrations.

Participants

2

Size

2.29 GB

Version

v1.0.0

Updated

Jul 10, 2026

A BIDS-formatted derivative dataset of steady-state visually evoked potential (SSVEP) EEG recordings from 54 subjects across 2 sessions. This dataset comprises 21,600 trials of 4-second EEG recordings at 1000 Hz sampling frequency from 62 channels, organized for standardized brain-computer interface benchmarking. The data has been processed and converted to BIDS format using the Mother of All BCI Benchmarks (MOABB) framework, enabling reproducible analysis of SSVEP-based BCI paradigms with 4 stimulus classes.

Participants

54

Channels

62 (10-05)

Citations

100

Size

16.9 GB

Version

v1.0.2

Updated

Jul 10, 2026

BigP3BCI Study A is a derivative P300 brain-computer interface dataset containing 32-channel EEG recordings from 13 healthy subjects performing a visual speller task using a 6×6 checkerboard stimulus paradigm. The dataset comprises EEG data sampled at 256 Hz with binary classification of target and non-target events, annotated using HED 8.4.0 schema. This represents Study A of the largest public P300 BCI dataset and has been processed using the MOABB (Mother of All BCI Benchmarks) framework.

Participants

13

Channels

32 (10-10)

HED

v8.4.0

Size

868 MB

Version

v1.0.1

Updated

Jul 10, 2026

This dataset comprises neuroimaging data from 8 participants performing functional localizer and movie-viewing tasks acquired simultaneously using Very High Density Diffuse Optical Tomography (VHD-DOT) and functional MRI. The dataset includes raw VHD-DOT data in SNIRF format from sessions 1-2 and fMRI data in NIfTI format from session 3 onwards, with preprocessed fMRI derivatives provided for direct comparison with optical neuroimaging measurements. Published in Imaging Neuroscience (2025).

Participants

8

Size

237 GB

Version

v1.0.0

Updated

Jul 10, 2026

The YOTO (You Only Think Once) dataset is a human electroencephalography resource comprising high-resolution EEG recordings from 20 participants performing multisensory perception and mental imagery tasks. Signals were acquired at 1000 Hz sampling rate during exposure to unimodal (visual and auditory) and multimodal stimuli, with participants providing subjective vividness ratings. Technical validation through event-related potentials and power spectral density analyses confirmed distinct neural responses across stimulus conditions, supporting applications in neural decoding, perception, and cognitive modeling.

Participants

20

Channels

30

Size

9.91 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises neurophysiological recordings from 39 participants who received calibrated laser stimuli while rating pain intensity and unpleasantness. Participants were presented with laser stimulations at individually-determined energy levels and provided subjective pain ratings on a 0-10 numeric rating scale. The study investigates the neural mechanisms underlying pain perception and assessment, with potential applications to understanding movement-induced hypoalgesia and related pain modulation phenomena. This is a derivative dataset based on OpenNeuro ds005289.

Participants

39

Channels

64 (10-10)

Size

22.1 GB

Version

v1.0.0

Updated

Jul 10, 2026

The MNE-Sample-Data dataset comprises simultaneous MEG and EEG recordings acquired from a single subject using a Neuromag Vectorview system at the Martinos Center for Biomedical Imaging. The experiment involved visual stimulation (checkerboard patterns presented to left and right visual fields) and auditory stimulation (tones to left and right ears), with occasional face stimuli requiring motor responses. Structural MRI data from a 1.5 T Siemens scanner and Freesurfer-derived anatomical derivatives are included, providing a comprehensive reference dataset for MEG/EEG analysis and method development.

Participants

2

Size

1.93 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises resting-state, eyes-closed scalp EEG recordings from 88 subjects: 36 with Alzheimer's disease, 23 with Frontotemporal Dementia, and 29 healthy controls. Recordings were acquired using a Nihon Kohden EEG 2100 device with 19 electrodes at 500 Hz sampling rate, totaling approximately 1164 minutes of data. The dataset includes both raw and preprocessed versions with artifact correction, supporting machine learning and clinical research in dementia diagnosis and neurophysiological characterization.

Participants

88

Channels

19 (10-20)

Citations

230

Size

5.38 GB

Version

v1.0.0

Updated

Jul 10, 2026