Showing 10 of 764 datasets · page 60 of 77
Resting-state 62-ch EEG, 215 subjects, eyes-open/closed blocks. BrainVision actiCHamp 2500Hz. DOI:10.1038/sdata.2018.308
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
64-ch EEG, 95 subjects, 2 sessions, 6 paradigms (13 tasks). BrainAmp 250Hz Easycap 64-ch. DOI:10.1016/j.neuroimage.2022.119666
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)
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)
BIDS-EMG dataset - HDsEMG recordings of tibialis anterior during isometric contractions at 30% and 50% MVC, 6 subjects, 256-channel grids (MUniverse benchmark)
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.
BigP3BCI Study P is a P300-based brain-computer interface dataset comprising EEG recordings from 19 healthy subjects across 2 sessions each, using a 9x8 character grid spelling paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz with target and non-target visual stimuli annotated using HED 8.4.0 schema. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset, and is optimized for machine learning applications in BCI research.
This 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.
HEFMI-ICH is a hybrid EEG-fNIRS motor imagery dataset designed for brain-computer interface applications in intracerebral hemorrhage rehabilitation. The dataset comprises 37 participants (17 healthy controls and 20 ICH patients) performing 2-class hand motor imagery tasks (left and right hand grasping) across multiple sessions. With 32-channel EEG recordings at 256 Hz and 3,330 trials, this dataset supports the development and evaluation of BCI systems for clinical stroke rehabilitation.