Sheng Hua Wang

This study investigates the relationship between delta-band synchrony and beta-gamma bistability in sleep-related epilepsy, finding they synergistically contribute to seizure generation.

The paper examines how REM sleep affects large-scale brain network dynamics and links this to its reduced epileptogenicity by analyzing SEEG recordings from epilepsy patients.

CROCOpy is a Python toolbox for analyzing neuronal oscillations and functional connectivity metrics in electrophysiological data.

This study uses advanced EEG analysis to assess cortical maturation in preterm infants by examining oscillatory dynamics during different vigilance states.

The paper develops a probabilistic model to identify epileptogenic networks using interictal SEEG data, achieving high classification accuracy with a reduced feature space.

The paper uses SEEG data to identify stable, frequency-specific modules in phase-synchronization connectomes that are anatomically contiguous and functionally related.

The paper investigates how ADHD symptoms relate to brain criticality, finding that intermediate symptom scores correlate with peak criticality and potential functional benefits.

The paper shows that individual differences in brain synchronization levels correlate with power-law long-range temporal correlations, with variations in healthy and epileptic brain regions.

This paper identifies critical-like bistable dynamics in the resting-state human brain, showing their functional and clinical significance across frequency bands.

This study shows that high-gamma activity (HGA) in human cortex can be synchronized across distant brain regions, revealing a new form of neuronal communication distinct from lower-frequency synchronization.

The paper identifies genuine cross-frequency coupling networks in human resting-state brain activity using MEG and SEEG data, showing distinct mechanisms for coupling oscillations across frequencies.

The paper shows that even advanced MEG/EEG connectivity measures can produce false positive 'ghost' interactions due to signal mixing, and quantifies this effect through simulations.

This paper introduces hyperedge bundling to reduce spurious interactions in MEG/EEG source connectivity analyses, improving the accuracy of functional connectivity networks.