100 results for "crossmodal perception" · page 5 of 10 · ranked by relevance
…As expected, athletes showed a significant reduction in pain perception after HIIT…
A multimodal EEG dataset comprising motor imagery and motor execution data from 25 healthy subjects performing 11 intuitive upper-limb movement tasks (6 reaching, 3 grasping, 2 wrist twisting) across 3 sessions. The dataset includes 71-channel EEG recordings (60 EEG, 4 EOG, 7 EMG channels) sampled at 1000 Hz with synchronized behavioral annotations, designed for brain-computer interface research and motor control applications. This is a BIDS-formatted derivative dataset converted from the original Jeong et al. 2020 publication using MOABB (Mother of All BCI Benchmarks).
…True ## Tags - **Modality**: visual - **Type**: perception ## Documentation - **Description**: BigP3BCI: the largest public…
This dataset presents multimodal simultaneous fMRI/EEG data collected from participants performing an auditory category learning task, alongside resting-state fMRI scans. Each dataset includes 63-channel EEG with additional ECG, EOG, facial EMG, and skin conductance recordings, a physiological file capturing respiration and finger-pulse signals, and structural MRI (T1-weighted and PD-weighted UTE) scans for anatomical reference and electrode localization. Collected at the Combinatorial NeuroImaging core facility of the Leibniz Institute for Neurobiology, the data support research into multi-dimensional auditory category learning and its neural correlates.
…capture neural activity manifest in perception, cognition, and action, in neurotypical young…
This dataset investigates the neural mechanisms underlying anticipatory processing by comparing attention and expectation effects during a cognitive task. The study employs neuroimaging to dissociate how top-down attentional control and predictive expectation modulate brain activity in preparation for upcoming stimuli, providing insights into fundamental mechanisms of anticipatory cognition.
[ 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.
[, eye-tracking, photoplethysmography (PPG), and galvanic skin response (GSR) data were acquired, synchronized via hardware TTL pulses. The dataset supports research into physiological and neural correlates of smartphone use and passive video viewing.