Single trial analysis of Readiness Potentials using Empirical Mode Decomposition

Since discovery of the slow negative electroencephalographic (EEG) activity preceding self-initiated movement by Kornhuber and Deecke, many studies arose to investigate the relationship between this signal, called Readiness Potential or \textit{Bereitschaftspotential}, and free will; in the early 80's, Benjamin Libet designed an experiment that allowed him to demonstrate that the intention to perform a movement begins about half a second before the actual movement is initiated. therefore, if it were possible to narrowly detect the presence of intention, we could gain important information about the state of consciousness of patients that present the locked-in syndrome (LIS) and ultimately it could be useful to discriminate between these patients and patients in a vegetative state.\\ In order to do so, the present thesis work proposes a general framework for the analysis of movement intention from the EEG signal; specifically, we meant to retrieve the Readiness Potential both from the average over many trials and from a single trial recording. Given the very low signal-to-noise ratio, single-trial analysis represents a challenging task: in this work, we present a detector, based on the Empirical Mode Decomposition (EMD) framework and spatial filters, that allowed for a better characterization of the RP, and resulted in significant noise reduction.