on005776
Electrical_Thermal_FingerTapping_2015
NIRS
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005776-blue…
- Participants
- 11
- Size
- 1.20 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
Discover datasets
100 results for "pediatric neuroimaging" · page 8 of 10 · ranked by relevance
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- nm000199
Learning from label proportions for a visual matrix speller (ERP)
EEGThis dataset comprises event-related potential (ERP) recordings from 13 healthy subjects performing a visual matrix speller task using a calibrationless brain-computer interface approach. The study introduces learning from label proportions (LLP), an unsupervised classification method that exploits known target/non-target stimulus ratios to enable online BCI operation without prior calibration. Subjects performed copy-spelling tasks using a 6×7 character grid across three sessions, achieving 84.5% character accuracy without labeled training data.
- Participants
- 13
- Channels
- 31 (10-10)
- Citations
- 46
- HED
- v8.4.0
- Size
- 5.15 GB
- Version
- v1.0.2
- Updated
- Aug 18, 2026
- on003039
free walking study
EEG[
- Citations
- 22
- Size
- 7.82 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000252
Imagined speech EEG dataset — long words condition (Nguyen et al. 2017)
EEGThis dataset comprises EEG recordings from 6 healthy participants performing imagined speech tasks involving two conditions: cooperate and independent word imagery. The study employed a motor imagery paradigm with auditory and visual cues, yielding 3,600 preprocessed trials (1,800 per class) recorded at 256 Hz from 64 channels. Data were analyzed using Riemannian manifold methods and relevance vector machines for brain-computer interface applications, achieving mean classification accuracy of 66.2±4.8%.
- Participants
- 6
- Channels
- 60 (10-10)
- Citations
- 97
- HED
- v8.4.0
- Size
- 1.27 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- on006673
ball_squeeze_Carlton_2025
NIRS[
EEGA high-density 124-channel EEG dataset comprising event-related potentials (ERPs) from 10 healthy participants during a visual object recognition task. Participants viewed 5,184 photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, and inanimate objects), with 72 photographs per category, presented for 500 ms each. The dataset is suitable for investigating neural representations of object categories through single-trial EEG classification and representational similarity analysis.
- Participants
- 10
- Channels
- 124 (egi-geodesic)
- Citations
- 93
- HED
- v8.4.0
- Size
- 3.40 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- on004830
Spatial Attention Decoding using fNIRS During Complex Scene Analysis
NIRS[ 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
- on005473
29 By BP
EEG[
- Size
- 24.0 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000158
Liu, Lv et al. 2023 — EEG datasets of stroke patients (motor imagery)
EEGThis dataset comprises electroencephalography (EEG) recordings from 50 acute stroke patients (1-30 days post-stroke) performing motor imagery tasks of left- and right-handed hand-grip movements. Recorded using a wireless 29-channel EEG system at 500 Hz, the dataset includes raw and preprocessed data, representing the first open resource addressing left- and right-handed motor imagery in the acute stroke population. The dataset supports brain-computer interface (BCI) algorithm development and clinical rehabilitation applications.
- Participants
- 50
- Channels
- 29 (10-10)
- Citations
- 78
- HED
- v8.4.0
- Size
- 1.15 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026