PhysioMotion_Artifact
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78 results for "stereoelectroencephalography" · page 8 of 8 · ranked by relevance
A longitudinal motor imagery EEG dataset from 18 brain-computer interface (BCI)-naive subjects acquired across 6 sessions (1 offline + 5 online) to investigate transfer learning and domain adaptation for calibration-free BCI training. Subjects performed left/right hand motor imagery tasks with visual feedback using 22 EEG channels sampled at 512 Hz. The dataset compares Generic Recentering (unsupervised) and Personally Assisted Recentering (supervised) domain adaptation frameworks, with features extracted as covariance matrices and classified using Riemannian geometry-based methods.
A multi-day, high-quality EEG dataset for motor imagery brain-computer interface research comprising 51 healthy subjects performing left and right hand motor imagery tasks across three sessions, with an additional 11 subjects performing 3-class motor imagery (left hand, right hand, foot). The dataset includes 59 EEG channels sampled at 1000 Hz with standardized 10-05 electrode montage, totaling 39,600 trials (51 subjects × 3 sessions × 200 trials for 2-class + 11 subjects × 3 sessions × 300 trials for 3-class) with visual and auditory cues. This resource is designed to support the development and benchmarking of motor imagery BCI algorithms and classifiers.
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 1-6 sessions per subject. With 32-channel EEG recordings at 256 Hz and approximately 3,330 trials, this dataset supports the development and evaluation of BCI systems for clinical stroke rehabilitation.
This dataset comprises electroencephalography recordings from 16 subjects viewing rapid serial visual presentations of gabor-like stimuli at two presentation rates (6.67 Hz and 20 Hz). Participants performed a concurrent fixation color change detection task while EEG data were acquired. The study investigates the dynamics of visual feature coding and neural mechanisms underlying perception and feature integration.
Imported from OpenNeuro ds003800