Mohamad Ali Khalil

This study examines how methodological choices in EEG source connectivity analysis affect results consistency and similarity across subjects.

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 evaluates how EEG channel density, inverse solutions, and connectivity measures affect resting-state network reconstruction accuracy using simulations.

The paper evaluates EEG source-space networks using a large-scale brain activity model called COALIA, showing that high electrode density and specific inverse solution/connectivity measure combinations improve network estimation.

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.

This paper introduces a framework to detect fast modular brain states during rest and task, validated on MEG and EEG data from healthy subjects and Parkinson's patients.