Maria L. Bringas-Vega

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 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 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 study analyzes multinational qEEG data to determine if country-specific developmental equations are needed, finding that age and frequency are more significant than country.