Biopsicosegnali e big data analisi

The course aims to provide the fundamental tolls and knowledge to understand and use the main biosignal analysis techniques used to analyze cognitive and behavioural functions, and to identify biopsychosignal patterns during different tasks, in experimental and clinical conditions. In addition, the course will introduce the main machine learning techniques to extract valuable information from the integration between biopsychosignals and clinical variables, or patient reported outcomes in order to highlight the underlying neurophysiological mechanisms.

D1 - Knowledge and understanding: By the end of the course, the student will have learned the basic principles of signal processing and the fundamental machine learning techniques in the context of biopsychosignals.
D2 - Ability to apply knowledge and understanding: By the end of the course, the student will be able to use and make simple modifications to signal processing and machine learning scripts.
D3 - Autonomy of judgment: The student will be able to identify the different types of biopsychosignals and their characteristics, to analyze the differences between different types of signal processing, and to identify the best suitable machine learning methodology to be applied for big data analysis
D4 - Communication skills: By the end of the course, the student will be able to describe the topics covered with appropriate language.
D5 - Learning ability: The student will be able to understand the benefits and properties of computational processing of a digital signal and of the correct management and storage of data.