OWM-Dataset
[ integrates magnetoencephalography (MEG), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) data collected from the same subjects viewing naturalistic stimuli from ImageNet. This trimodal neuroimaging resource provides both high spatial resolution (fMRI) and high temporal resolution (MEG/EEG) for investigating object recognition mechanisms. The dataset encompasses diverse naturalistic stimuli and a broad subject pool, facilitating exploration of neural activation patterns across stimuli and subjects. The EEG component is available as a separate dataset (ds005811).
[ dataset comprising recordings from 24 subjects totaling over 17 hours of data acquired using a KIT/Yokogawa system with 208 magnetometer channels. The dataset is designed to support morpheme-based linguistic analysis and investigation of language and cognitive processes through visual presentation paradigms, providing a resource for studying neural mechanisms of language processing at fine temporal resolution.
This EEG-BIDS dataset contains cue-locked recordings from 43 neurotypical adults performing a random-dot kinematogram task designed to dissociate low-level and high-level priors during perceptual decision-making. The study investigates how hierarchical priors bias behavior and modulate occipital oscillatory and aperiodic EEG activity using signal detection theory and generalized drift-diffusion modeling. Three within-subject conditions (baseline, low-level prior, high-level prior) were tested with individualized motion coherence thresholds.
This multimodal neuroimaging dataset investigates the neural mechanisms of metacognition—the ability to assess decision confidence—by isolating postdecisional from decisional contributions. Healthy volunteers performed perceptual judgments and observed decisions while reporting confidence, with concurrent electroencephalography and functional magnetic resonance imaging recordings. The study reveals dissociable neural correlates of confidence in prefrontal regions and proposes a computational model explaining how decision commitment enhances metacognitive performance.