Thesis title: Analysis of Brain Activity and Neurophysiological States using Electroencephalography and Magnetic Resonance Imaging Data.

Abstract: The PhD thesis addresses the analysis of brain activity and the classification of neurophysiological states in healthy individuals and patients, exploiting electroencephalography (EEG) and diffusion tensor imaging (DTI) data and employing methods from statistics, machine learning, and deep learning. Based on these techniques, this work aims to discriminate between different states of brain function and identify significant features, focusing on cognitive processes related to memory, mental fatigue, real and imagined movement, as well as on overall brain connectivity. The objectives include the investigation of the applicability of user-friendly cognitive training protocols, the utilisation of brain-computer interfaces for the study and support of motor functions, and the analysis of brain reorganisation at the structural level due to disease and therapeutic interventions. Overall, the thesis highlights the potential for interpretable classification and analysis of brain states based on electrophysiological and neuroimaging data, in combination with the exploitation of behavioral cognitive metrics and the application of different protocols covering cognitive training, mental fatigue analysis, motor function analysis, and the study of the effects of disease and therapeutic approaches.

Supervisor: Professor George Matsopoulos

PhD Student: Stavros Miloulis