Judie Tabbal

The paper identifies three neurophysiological biotypes in youth using EEG data, revealing brain-behavior relationships that challenge traditional diagnostic categories.

This study uses EEG and normative modeling to identify deviations in neurophysiological features in Parkinson's and Alzheimer's patients, showing significant heterogeneity and correlations with clinical scores.

This paper quantifies EEG heterogeneity in psychiatric disorders using normative modeling, finding significant individual deviations that correlate with clinical assessments.

This study uses HD-EEG and machine learning to identify resting-state brain networks predictive of real-life creativity, finding significant correlations in both internal and external validation.

This study uses dynamic mode decomposition to analyze MEG data and shows it provides better temporal resolution than PCA for task-related brain network dynamics.

The paper evaluates EEG/MEG source-space network analysis techniques using a human brain computational model and identifies optimal methods for inverse modeling, connectivity measures, and dimensionality reduction.

This study investigates dynamic functional brain networks during cognitive action control in Parkinson's disease patients and healthy controls using HD-EEG, finding differences in network composition and temporal organization.

This study benchmarks nine source separation methods on MEG data to identify the most effective for tracking fast electrophysiological network dynamics during motor and memory tasks.