Acquisition and Analysis Protocol Optimization of Neurophysiological Data Neurorehabilitation Settings

The study aims to optimize and validate protocols for acquiring neurophysiological data, specifically resting state functional connectivity, using advanced research techniques (hdEEG and MEG) and a user-friendly device (MUSE). Previous studies have extensively explored functional connectivity repeatability in resting conditions using functional MRI, yet few have focused on hdEEG and MEG data. Additionally, the impact of subjects' eye conditions (open or closed) during resting state recordings on network identification remains debated. The investigation involves assessing the effect of eye conditions on brain network identification and determining the most stable and repeatable measures of functional connectivity over time. This analysis is crucial for discerning whether observed changes in patients' functional connections are intrinsic to the methodology or indicative of genuine physiological alterations. The study aims to optimize protocols for rehabilitation by evaluating changes in functional connectivity metrics during and between experimental sessions. Furthermore, it seeks to identify the conditions (eyes closed or open) that yield more reliable and repeatable functional measurements. Following the optimization of advanced techniques, the study explores the feasibility of utilizing the MUSE EEG system in clinical settings. MUSE, known for its portability and user-friendliness, has demonstrated quality in experimental psychology and clinical research. The objective is to establish relevant functional correspondences between measurements obtained through research techniques (hdEEG and MEG) and those acquired with MUSE. The primary goal is to establish a protocol highlighting subjects' responses to acoustic stimuli or a reproducible pattern of resting state activity. The secondary objectives include investigating temporal and spatial characteristics of neurophysiological signals in healthy subjects over time and defining prognostic biomarkers for monitoring p