Serge Vulliémoz

This study evaluates how well electrical source imaging reconstructs neural time series using simultaneous scalp and intracranial EEG recordings, finding limited temporal reconstruction and spatial leakage.

This study shows that wetting hair with tap water improves dry electrode EEG signal quality by reducing impedance and increasing spectral and functional connectivity similarity to gel-based systems.

This paper develops a whole-brain model to predict conduction velocity from MRI-derived microstructural features, explaining 29% of variance in neurophysiological conduction velocity.

GEDAI improves EEG denoising by removing artifacts without clean references, outperforming existing methods in simulations and enhancing neurobehavioral predictions.

The paper examines multi-timescale functional connectivity dynamics in the brain using EEG and fMRI data, finding spatial convergence but temporal divergence across different FC timescales.

The paper examines the relationship between intracranial EEG and fMRI in the sensorimotor cortex, finding that high gamma power correlates with fMRI changes during motor tasks and rest.

This study uses MEG and blind source separation to identify mesial brain activity linked to memory tasks, validated with intracerebral EEG.

This paper introduces the connectome spectrum as a method to represent brain activity using structural connectivity, showing it provides a more compact and informative representation of fast brain dynamics.

This study shows that EEG and fMRI connectomes are reproducibly correlated across different simultaneous EEG-fMRI setups, including varying magnetic field strengths and electrode densities.

This paper introduces a method using network harmonics to track fast EEG dynamics during a face detection task with subsecond resolution.

The paper uses white matter structural connectivity to improve EEG functional connectivity by smoothing signals in a graph space derived from structural data, increasing resemblance to fMRI connectivity.

The paper investigates early brain network alterations in toddlers and preschoolers with autism using gaze patterns and brain activity during social scene exploration, finding increased hyper-activity and hyper-connectivity in social brain networks.