Our research focuses on the development and application of advanced signal processing methods for the analysis of human electrophysiological recordings, whether invasive (iEEG) or non-invasive (EEG and MEG), specifically in the context of epilepsy.
Brain Connectivity: Epilepsy is a brain network disease and brain connectivity appears to be an idea tool to characterise epileptic brains. Non-invasively derived brain connectivity metrics need to be validated with a gold standard. Our aim is to take advantage of the rare simultaneous hd-EEG-iEEG recordings to validate them.
Postsurgical Outcome Prediction: By combining connectivity/network analysis and clinical data, we aim to develop a machine learning algorithm, coupled to a clinical report, to predict which patients might benefit from resective surgery.
Asymmetry of sleep electrophysiological markers in patients with focal epilepsy.
Cortico-cortical and thalamo-cortical connectivity during non-REM and REM sleep: Insights from intracranial recordings in humans.
Aberrant Developmental Patterns of Gamma-Band Response and Long-Range Communication Disruption in Youths With 22q11.2 Deletion Syndrome.
Phase-Amplitude Coupling and Phase Synchronization Between Medial Temporal, Frontal and Posterior Brain Regions Support Episodic Autobiographical Memory Recall.
Département de Neurosciences cliniques