Mitchell Joseph Valdés-Sosa

The paper introduces {xi}-Net, a generative model of cortical activity that estimates EEG components and connectivity across lifespan using rsEEG data.

The paper compares EEG patterns between COVID-19 survivors, close contacts, and a normative database to identify brain function impairments and abnormal activity patterns.

The paper uses fMRI data to predict Navon letter stimuli from intra-V1 functional networks, showing inter-individual stability but intra-individual variability in shape prediction.

The paper examines intra-V1 networks during perceptual tasks, finding condition-specific connection strengths linked to visual field regions.

This paper develops multinational qEEG norms using Hermitian Riemannian methods, showing improved diagnostic accuracy for brain dysfunction and providing harmonization tools.

The paper identifies cortical regions specialized for processing global and local shape levels in visual objects and shows they form distinct functional pathways within the ventral-temporal-cortex.