Mahmoud Hassan

This study evaluates how electrode density impacts EEG analysis across multiple levels, finding that lower densities affect regional and source-level analyses more than global measures.

The paper maps lifespan EEG trajectories across age groups, revealing nonlinear patterns and spatial variations in brain activity.

The paper shows that EEG-brain behavior associations are unstable with small samples but become reliable with large cohorts.

The paper examines how beta-band burst dynamics in the motor cortex change with motor skill learning under adaptive and non-adaptive training conditions, showing reorganization of burst features with practice.

The paper shows that small sample sizes in EEG case-control studies lead to unreliable results, while larger samples produce consistent and reproducible findings.

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.

The paper identifies altered functional connectivity patterns in young children with ASD using EEG data and links them to cognitive, social, and sensory dimensions of the disorder.

This study predicts nonverbal intelligence levels from EEG data using connectome-based modeling, finding significant variability in accuracy based on processing pipelines and highlighting the importance of alpha frequency bands and frontal-parietal regions.

This study uses high-density EEG to analyze perceptual, attentional, and working memory processes during motor learning with random vs. blocked practice schedules.

The paper investigates how random practice schedules affect neural efficiency in perceptual and attentional processes using high-density EEG analyses.

This study analyzes dynamic brain connectivity in psychosis using resting-state EEG, finding altered network states and correlations with negative symptoms and cognitive functioning.

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

This study identifies electrophysiological signatures of anxiety in Parkinson's disease using resting-state EEG, showing altered power and connectivity patterns associated with clinical anxiety scores.

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

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 paper reanalyzed EEG data using three software tools to assess how preprocessing variability affects results.

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.

This study tracks changes in electrophysiological brain networks during 6 weeks of motor skill training and finds increased frequency-specific flexibility in brain regions associated with learning.

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.

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.

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 study examines dynamic brain modularity at rest using EEG/MEG, revealing spatiotemporal patterns and sub-networks within RSNs, and links dynamics to mental imagery.

The paper investigates conscious perception by analyzing brain networks during magnetophosphenes, finding distinct roles for alpha and beta band oscillations in integration and segregation.

This study uses dynamic functional connectivity measures from EEG and MEG data to show that personality traits like neuroticism, extraversion, and openness correlate with brain network dynamics.

The paper evaluates how different functional connectivity methods affect EEG source space networks at rest and finds that methods retaining zero-lag connections correlate better with fMRI networks.

This study examines functional brain network changes in disorders of consciousness using EEG, finding reduced network integration and increased segregation in patients compared to healthy controls.

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.