on004350
Executive Functionning Study for Assessing the Effect of Neurofeedback
EEG
- Participants
- 24
- Channels
- 64 (10-10)
- Citations
- 48
- Size
- 26.4 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
Discover datasets
100 results for "neuroimaging" · page 5 of 10 · ranked by relevance
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- on002312
OcularLDT
MEG[
- Citations
- 74
- HED
- v8.4.0
- Size
- 1.08 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- on006545
Reliability-Dubois2024
NIRS[
EEG- Participants
- 50
- Channels
- 29 (10-10)
- Citations
- 73
- HED
- v8.4.0
- Size
- 673 MB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026
- on003483
Logical reasoning study
MEG<!-- nemar:provenance --> ## Provenance This dataset is mirrored on NEMAR from OpenNeuro. - Source…
- Participants
- 21
- Size
- 68.3 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- on005274
UV_EEG
EEG[
- Size
- 71.8 MB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- nm000270
Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University)
EEGThis dataset comprises scalp electroencephalography recordings from 27 stroke patients performing lower-limb motor imagery tasks as part of a longitudinal rehabilitation training protocol. The multi-paradigm design supports research on motor-imagery brain–computer interfaces for gait and lower-limb rehabilitation following stroke, with repeated sessions enabling investigation of learning and neuroplasticity during motor recovery.
- Participants
- 27
- Citations
- 20
- Size
- 5.79 GB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026
- on005410
Semantic_conditioning
EEG[
- Size
- 19.8 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- nm000220
BEETL Competition 2021 Motor Imagery Dataset A — transfer learning benchmark
EEGBeetl2021-A is a preprocessed motor imagery EEG dataset from the BEETL Competition Task 2 (NeurIPS 2021), comprising data from 3 healthy subjects collected during an online racing game (Cybathlon2020IC). The dataset contains 63-channel EEG recordings at 500 Hz with four-class motor imagery tasks (rest, left hand, right hand, feet) and serves as a benchmark for evaluating transfer learning and domain adaptation methods across heterogeneous EEG datasets and subjects. This dataset is part of a larger competition focused on advancing transfer learning for subject independence and cross-dataset generalization in brain-computer interfacing.
- Participants
- 3
- Channels
- 63 (10-10)
- HED
- v8.4.0
- Size
- 216 MB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026