…wheelchairs, and even drones by decoding motor imagination from electroencephalography (EEG). Large…
…A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding…
…Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography…
…Movement-related bad trials rejected for incorrect direction, no movement, duration <0…
…Recent neuroimaging evidence has shown that goal-directed movement is an important…
HAD-MEEG is a magnetoencephalography (MEG) and electroencephalography (EEG) dataset on human action recognition. It extends the Human Action Dataset fMRI resource (HAD-fMRI) and was recorded in the same 30 participants with the same stimuli, 21,600 video clips spanning 180 categories of human action. The millisecond temporal resolution of M/EEG complements the spatial precision of fMRI, enabling spatiotemporal study of the neural mechanisms of action recognition.
…Recent neuroimaging evidence has shown that goal-directed movement is an important…
…Brain-computer interface (BCI), Electroencephalography (EEG), Handwriting, Continuous movement decoding, Non-invasive…
…to-end EEG decoding of imagined or executed movements from raw EEG…
This dataset contains functional near-infrared spectroscopy (fNIRS) recordings collected to evaluate motion artifacts in long-term clinical NIRS monitoring. It accompanies a study comparing collodion-fixed prism-based optical fibers with conventional fiber attachment for reducing motion artifacts. The data are mirrored on NEMAR from OpenNeuro.