Identifying EEG Biomarkers in ALS: Development of Computational Tools for Experimental Signal Analysis

This thesis focuses on the analysis of brain signals through EEG, aiming to identify potential biomarkers associated with Amyotrophic Lateral Sclerosis (ALS). After a brief overview of the neurodegenerative nature of the disease, the main diagnostic approaches based on biomarkers are presented. Starting from the collection of experimental data in the lab, the study describes the metrics used and the methods for their extraction and interpretation. The architecture of the algorithm developed for EEG signal analysis is also outlined, with a focus on its logical structure. Finally, the main results are presented and discussed, with particular attention to the potential of EEG as a tool for ALS diagnosis and monitoring.