Lateralized Readiness Potentials

The goal of this thesis is to investigate and improve the computation of the Lateralized Readiness Potential by using tools like Machine Learning and Dimensionality Reduction side by side with the traditional tools usually em- ployed in neurophysiology, such as ??ltering and trials averaging. Within this work Lateralized Readiness Potentials (LRP) are investigated with the intention of understanding whether, in a near future, they could be used as a diagnostic tool in motor and consciousness disorders. A typical scenario could be the diagnosis of a a Locked-In Syndrome. Locked-in syndrome (LIS) is a condition in which a patient has a complete paralysis of almost all voluntary muscles except for vertical eye movements and blinking. Locked-in syndrome may be confused with a loss of conscious- ness in patients, thus misleading to a diagnosis of Vegetative State may even resemble death. Being the LRP, or more in general RP, associated the intentionality of the movement, they could unveil if there still consciousness in what could be misunderstood as a muscle spasm. During this work of thesis the use of several methods and techniques was in- vestigated. All these methods will be shown in the following, with particular emphasis on the ones employed in the software developed and on the reason why they were preferred to the others.