Disentangling the percepts of illusory movement and sensory stimulation during tendon vibration in the EEG
Imported from OpenNeuro ds003343
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47 results for "hemispheric lateralization" · page 3 of 5 · ranked by relevance
Imported from OpenNeuro ds003343
A multimodal neuroimaging dataset designed to investigate the spatiotemporal dynamics of visual processing in humans. The dataset combines multiple neuroimaging modalities—electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and structural MRI—to characterize neural responses during visual tasks. This comprehensive resource provides simultaneous EEG-fMRI recordings that enable investigation of the temporal and spatial organization of visual cortical processing, bridging the high temporal resolution of EEG with the high spatial resolution of fMRI.
BCIComp2020UpperLimb is a preprocessed EEG dataset from BCI Competition 2020 Track 4 containing motor imagery recordings of three upper-limb grasping tasks (cylindrical, spherical, lumbrical) from 15 healthy subjects across three sessions. The dataset comprises 60-channel EEG data sampled at 250 Hz with 450 trials per subject (150 trials per session × 3 sessions), designed to evaluate session-to-session transfer learning in brain-computer interface applications. Data were preprocessed with 60 Hz notch filtering and cue-aligned epoching, with the 4-second motor imagery window extracted for analysis.
This magnetoencephalography (MEG) dataset investigates the differential neural mechanisms underlying selection and maintenance of information during working memory tasks. Data from 22 participants include MEG recordings across two sessions, structural MRI, and detailed behavioral measures from a working memory task and a one-back control task involving visual Gabor stimuli. The dataset supports investigation of how the brain selectively maintains task-relevant information while filtering distractions.
This high-density functional near-infrared spectroscopy (fNIRS) dataset comprises neuroimaging recordings from the motor cortex during three experimental paradigms: resting state, ball-squeezing motor tasks (both hands performed sequentially), and purposeful motion artifact creation. The dataset includes concurrent accelerometer measurements and is designed to support the development and validation of motion artifact correction algorithms in fNIRS neuroimaging.
The MNE-Sample-Data dataset comprises simultaneous MEG and EEG recordings acquired from a single subject using a Neuromag Vectorview system at the Martinos Center for Biomedical Imaging. The experiment involved visual stimulation (checkerboard patterns presented to left and right visual fields) 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.