QuantSLEEP: A numerical toolbox for a graph signal processing of the rhythmic and arrhythmic components of sleep EEG recordings.

点击翻译按钮获取中文摘要

A third of human life is spent sleeping. Far from being a resting state, sleep is a particularly important state during which the brain generates and processes information, and undergoes a plastic reorganization of synaptic connections to be more efficient during the forthcoming awake time. In terms of signal processing, sleep is a pristine `laboratory' in which electric measurements of spontaneous brain activity give a clear signature of the ability of the brain to cope with, and to respond to adverse threats during the previous periods of wakefulness. Today, our understanding of this activity is mostly a matter of sequences of interactions between various processes. In addition to the possible coupling of bursts of oscillations, the brain bioelectrical activity also shows interactions between short-lived functional networks that organize the co-activated remote brain areas involved in the neuronal processes. Our research program will focus on the development of numerical methods for the integration of advanced signal processing specific to electro-encephalography (EEG) measurements of the sleeping brain. More specifically, this research program aims at developing an innovative software to provide the time-resolved interactions between the networks involved during sleep. The networks will be obtained from different components of the EEG signals, from the characteristic transient rhythmic oscillations observable in the background of an arrhythmic component, to the more recent `microstates' events that happen to sample the stable co-activation of remote brain regions. The main outcome of this proposal is a numerical tool that integrates innovative approaches to decode EEG signals in order to provide the inventory and the dynamical interactions of the functional networks associated with the fundamental biomarkers of the `sleeping brain'. This innovative description of the sleep EEG recordings will be evaluated in various contexts, from the early maturation of the brain during childhood to clinical applications in epilepsy and other neuro-degenerative diseases.