This thesis is focused on the development of a Machine Learning (ML) model with the primary goal of accurately classifying voluntary and involuntary movements by means of the EEG signal, contributing to the larger project of diagnosing the level of consciousness in non-responsive patients. Some tens of thousands of patients across the globe have been incorrectly labeled as "awake but unaware" for long periods, when they have actually remained conscious the entire time [1]. The high prevalence of misdiagnosis highlights the need for improved diagnostic methods in this field. The goal is to minimize misdiagnoses, leading to improved patient outcomes and care in the field of Brain Injuries.