Discover datasets

Imported from OpenNeuro ds001787

Participants

24

Channels

64

Citations

13

Size

5.69 GB

Version

v1.0.0

Updated

Jul 10, 2026

Imported from OpenNeuro ds003800

Participants

13

Channels

19 (10-20)

Size

364 MB

Version

v1.0.0

Updated

Jul 10, 2026

MEG-MASC is a high-quality magnetoencephalography dataset comprising raw MEG recordings from 27 English speakers listening to approximately two hours of naturalistic stories from the Manually Annotated Sub-Corpus (MASC). The dataset includes precise temporal annotations of word and phoneme onsets/offsets, organized according to the Brain Imaging Data Structure (BIDS) standard. This benchmark dataset enables large-scale encoding and decoding analyses of neural responses to natural speech processing, with accompanying code for validation analyses including temporal decoding of phonetic features and word frequency effects.

Participants

27

Citations

46

Size

99.3 GB

Version

v1.0.1

Updated

Jul 10, 2026

fNIRS finger tapping with lateralized auditory cues. 5 subjects, 56 NIRS channels (8 sources, 16 detectors incl. 8 short), NIRx NIRScout, 7.8 Hz, SNIRF format. 3 conditions x 30 trials. Zenodo DOI: 10.5281/zenodo.6575155

Participants

5

Size

47.4 MB

Version

v1.0.0

Updated

Jul 10, 2026

BIDS-EMG dataset from Grison et al. 2025 - HDsEMG recordings of tibialis anterior during isometric contractions at 10-70% MVC, 1 subject, 128-channel grids (2x64), with concurrent intramuscular EMG ground truth labels (MUniverse benchmark)

Participants

1

Size

2.68 GB

Version

v1.0.1

Updated

Jul 10, 2026

BIDS-EMG dataset - HDsEMG recordings of tibialis anterior and vastus lateralis during isometric contractions at 10-80% MVC, 16 subjects, 256-channel grids (MUniverse benchmark)

Participants

16

Size

5.51 GB

Version

v1.0.1

Updated

Jul 10, 2026

BIDS-EMG dataset - HDsEMG recordings of tibialis anterior during isometric contractions at 30% and 50% MVC, 6 subjects, 256-channel grids (MUniverse benchmark)

Participants

6

Size

448 MB

Version

v1.0.1

Updated

Jul 10, 2026

A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing a P300-based speller task across three sessions. The dataset comprises 2,520 trials of visual event-related potential data acquired at 1000 Hz using a 64-channel Neuroscan system (64 EEG channels + 1 EOG + 1 stimulus channel), designed to enable analysis of ocular activity patterns and their relationship to brain-computer interface performance across multiple BCI paradigms.

Participants

31

Channels

62 (10-10)

Citations

11

HED

v8.4.0

Size

40.7 GB

Version

v1.0.3

Updated

Aug 18, 2026

A multimodal EEG dataset comprising motor imagery and motor execution data from 25 healthy subjects performing 11 intuitive upper-limb movement tasks (6 reaching, 3 grasping, 2 wrist twisting) across 3 sessions. The dataset includes 71-channel EEG recordings (60 EEG, 4 EOG, 7 EMG channels) sampled at 1000 Hz with synchronized behavioral annotations, designed for brain-computer interface research and motor control applications. This is a BIDS-formatted derivative dataset converted from the original Jeong et al. 2020 publication using MOABB (Mother of All BCI Benchmarks).

Participants

25

Channels

71 (10-10)

Citations

82

HED

v8.4.0

Size

326 GB

Version

v1.0.2

Updated

Aug 18, 2026

A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing a 4-class steady-state visually evoked potential (SSVEP) brain-computer interface task. The dataset comprises 3,024 trials across 63 sessions with 66-channel recordings (64 EEG + 1 EOG + 1 stim) at 1000 Hz sampling rate, designed to investigate ocular activity patterns and their relationship to BCI performance across multiple paradigms.

Participants

11

Channels

62 (10-10)

Citations

12

HED

v8.4.0

Size

28.6 GB

Version

v1.0.3

Updated

Aug 18, 2026