on006459
High-DensityvSparsefNIRS_WordColorStroop_Sparse_Anderson_2025
NIRS
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on006459-blue…
- Participants
- 17
- Size
- 169 MB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
Discover datasets
100 results for "pediatric neuroimaging" · page 9 of 10 · ranked by relevance
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- nm000134
Alljoined-1.6M
EEG- Participants
- 20
- Channels
- 32 (10-10)
- Citations
- 1
- Size
- 7.75 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on007990
Subjective tinnitus severity correlates with objective changes in human auditory cortex as measured by functional near-infrared spectroscopy
NIRS[
EEGAn open-access EEG dataset containing recordings from 15 healthy Spanish-speaking subjects performing imagined speech tasks. The dataset comprises 11 imagery classes (5 Spanish vowels and 6 directional commands) acquired using 6-channel EEG at 1024 Hz with preprocessed data (bandpass filtered 2-45 Hz). This BIDS-reformatted derivative is based on the original data described in Pressel et al. 2016 and supports brain-computer interface research and motor imagery classification studies.
- Participants
- 15
- Channels
- 6 (10-20)
- Citations
- 92
- HED
- v8.4.0
- Size
- 2.00 GB
- Version
- v1.0.4
- Updated
- Aug 18, 2026
- on007477
TimeSeries BIDS converted
NIRS[ — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
EEGA comprehensive EEG dataset comprising 54 healthy subjects performing three major brain-computer interface (BCI) paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP) across two sessions. The dataset investigates BCI illiteracy rates and performance variations, revealing that while MI showed the highest illiteracy rate (53.7%), all participants could control at least one BCI paradigm. Data were acquired at 1000 Hz using 62 EEG channels with concurrent electromyography recordings.
- Participants
- 54
- Channels
- 62 (10-05)
- Citations
- 99
- HED
- v8.4.0
- Size
- 121 GB
- Version
- v1.0.5
- Updated
- Aug 18, 2026
- nm000173
Motor Imagery ataset from Ofner et al 2017
EEGThis dataset comprises EEG recordings from 15 healthy subjects performing six different upper limb movements (elbow flexion/extension, forearm supination/pronation, hand open/close) and rest conditions in both movement execution and motor imagery modalities. The study investigates neural encoding of individual upper limb movements using low-frequency EEG signals (0.3-3 Hz) and achieves classification accuracies of 55-87% for executed movements and 27-73% for imagined movements. Source localization analysis identifies discriminative movement information in premotor areas, primary motor cortex, somatosensory cortex, and posterior parietal cortex, with applications toward non-invasive control of motor neuroprostheses and robotic arms.
- Participants
- 15
- Channels
- 61 (10-05)
- Citations
- 94
- HED
- v8.4.0
- Size
- 34.0 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- on005963
FRESH Motor Dataset
NIRS[
- Citations
- 99
- HED
- v8.1.0
- Size
- 11.1 GB
- Version
- v1.0.0
- 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 EEG recordings from 27 stroke patients performing a lower-limb motor imagery task as part of a multi-paradigm, longitudinal rehabilitation training protocol. Data were collected at Tianjin University to support research on motor-imagery brain-computer interfaces for gait and lower-limb rehabilitation after stroke. The dataset is organized in BIDS format with a single EEG task (task-imagery) across repeated sessions.
- Participants
- 27
- Channels
- 64 (10-10)
- Citations
- 23
- HED
- v8.4.0
- Size
- 56.7 GB
- Version
- v1.0.5
- Updated
- Aug 20, 2026