Machine learning of multi-channel electroencephalographic data

Machine Learning techniques have been recently applied in the healthcare field and particularly for electroencephalographic signal classification, opening new possibilities for brain activities and diseases analysis through peculiar applications like the Brain Computer Interfaces. The project proposal for the Ph.D. thesis work briefly described in the following wants to address the problems arising from these biomedical heterogeneous data, starting from the preliminary signal processing for noise removal, moving to possible data normalisation for subject and population based analysis and exploiting the outputted manipulated data to create classifiers for peculiar brain activities labelling, diseases identification, Brain Computer Interface development. These steps will require an evaluation of the state-of-the-art, which present mostly semi-automatic or manual signal processing techniques, that will be used to create fully automated denoising modules for every type of data and integrated for scenario-dependent signal reconstruction procedures. Also, there is a narrow number of studies addressing the normalisation problem, which is to be considered for population-based analysis. Finally, the recent works on electrophysiological signal classification will be used to evaluate commonly used Machine Learning algorithms and to create best-practices for feature extraction, a benchmark for deep learning techniques application and the study of Brain Computer Interface mainly for rehabilitation purposes