on006035
somatomotor
ANAT MEG
…Multi-subject, multi-modal (sMRI+MEG+EEG) neuroimaging dataset for median nerve…
- Participants
- 5
- Size
- 3.07 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
Discover datasets
100 results for "hybrid neuroimaging" · page 5 of 10 · ranked by relevance
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- nm000348
Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface
EEGThis dataset (WBCIC-SHU) contains multi-day EEG recordings from 51 healthy, right-handed, BCI-naive subjects performing a motor imagery brain-computer interface paradigm across three sessions per subject. Participants imagined left-hand, right-hand, or (for a subset of 11 subjects) foot movements in response to visual and auditory cues, yielding 39,600 trials in total. The dataset is intended for benchmarking motor imagery classification algorithms such as CSP, FBCSP, EEGNet, deepConvNet, and FBCNet, and was converted to BIDS format using MOABB.
- Participants
- 51
- Channels
- 59 (10-10)
- Citations
- 34
- HED
- v8.4.0
- Size
- 154 GB
- Version
- v1.0.5
- Updated
- Aug 18, 2026
- on004843
T16
EEG[
- HED
- v8.1.0
- Size
- 7.66 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on004624
Intracranial recordings using BCI2000 and the CorTec BrainInterchange
ANAT iEEGThis dataset comprises intracranial EEG recordings collected using the CorTec BrainInterchange implantable device and the BCI2000 software platform, developed as part of an ecosystem of technology and protocols for adaptive neuromodulation research in humans. The data support research into closed-loop neuromodulation approaches and adaptive brain-computer interface systems. Recordings were obtained under IACUC-approved protocols in collaboration with Mayo Clinic and academic partners.
- Participants
- 3
- Size
- 19.3 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- on003078
PROBE iEEG
ANAT iEEG[
- Citations
- 2
- Size
- 18.0 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000274
BEETL Competition 2021 Motor Imagery Dataset B — transfer learning benchmark
EEGBeetl2021-B is a preprocessed EEG dataset from the NeurIPS 2021 BEETL competition designed to benchmark transfer learning and domain adaptation algorithms for motor imagery decoding. The dataset comprises 32-channel EEG recordings from 2 healthy subjects performing 4-class motor imagery tasks (left hand, right hand, feet, and rest) sampled at 200 Hz, with 1590 total trials organized into 4-second epochs. This resource addresses the critical challenge of cross-subject and cross-dataset generalization in brain-computer interface applications.
- Participants
- 2
- Channels
- 32 (10-10)
- Citations
- 2
- HED
- v8.4.0
- Size
- 1.99 GB
- Version
- v1.0.4
- Updated
- Aug 18, 2026
- on002312
OcularLDT
MEG[ 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
- on006545
Reliability-Dubois2024
NIRS[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on006545-blue…
- Participants
- 49
- Size
- 46.7 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026