Ying Wang

The paper introduces JSPACE, a method for estimating source precision matrices from EEG data, and applies it to create a lifespan atlas of cortical interactions.

This paper introduces a statistical framework to reconstruct whole-brain iEEG activity from sparse, non-overlapping recordings, validated on multi-center data to recover known electrophysiological signatures.

This paper introduces a unified framework for batch harmonization in normative modeling, showing how existing methods can be expressed as special cases of a single location-scale equation.

This paper introduces GNM, a hierarchical framework for multi-site brain normative modeling that produces site-invariant z-scores and preserves age-related signals better than existing methods.

This paper introduces BiSCA to detect nonlinearity in EEG data and finds that narrowband oscillations like Alpha and Mu rhythms exhibit significant nonlinear behavior, while broadband activity is linear and Gaussian.

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

The paper analyzes EEG data from a large cohort to show aperiodic neural activity decreases monotonically with age, while periodic activity increases then decreases, mapping to distinct biological processes.

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

This paper introduces a method to control leakage in electrophysiological source imaging using structured sparse Bayesian learning and evaluates its performance against existing methods.