(D1 and D2) To know and be able to apply statistical as well as linear and nonlinear analysis tools to analyze biomedical data and to study neurosignals. (D3) Knowing how to assess the differences among different tecniques of signal analysis in order to select the more appropriate to the specific problem. (D4) Knowing how to explain the problems related to the analysis of biomedical signals in oral and written form. (D5) To be able to understand information contained in textbooks and other material that can be used in the analysis of biomedical data and signals.

Basic knowledge of signal theory.

Part 1: Tools and Statistical Parameters; Probability Distributions; Normal Plot; Statistical Inference; Confidence Interval; Optimal Sample Size; Hypothesis Testing; Parametric and Non-Parametric Tests; Tests for 1, 2, and More Than 2 Samples; Linear Regression Lines; Linear Correlation Coefficient Part 2: Signal Analysis: Linear and Non-Linear Methods: PSD (Power Spectral Density), Beta Exponent, Fractal Dimension, Poincaré Plot Part 3: Functional Neuroimaging Analysis: Introduction to Functional Neuroimaging, Basic Principles and Common Techniques, Specific Analysis Methods for Neuroimaging, Applications in Research and Clinical Practice Part 4: Biomedical and Clinical Data Analysis Methods: Machine Learning and Deep Learning Methods, Generative Artificial Intelligence - Concepts and Potential Applications, Examples and Practical Projects

Di Orio: Statistica Medica; Lecture slides

Part 1: Tools and Statistical Parameters; Probability Distributions; Normal Plot; Statistical Inference; Confidence Interval; Optimal Sample Size; Hypothesis Testing; Parametric and Non-Parametric Tests; Tests for 1, 2, and More Than 2 Samples; Linear Regression Lines; Linear Correlation Coefficient Part 2: Signal Analysis: Linear and Non-Linear Methods: PSD (Power Spectral Density), Beta Exponent, Fractal Dimension, Poincaré Plot Part 3: Functional Neuroimaging Analysis: Introduction to Functional Neuroimaging, Basic Principles and Common Techniques, Specific Analysis Methods for Neuroimaging, Applications in Research and Clinical Practice Part 4: Biomedical and Clinical Data Analysis Methods: Machine Learning and Deep Learning Methods, Generative Artificial Intelligence - Concepts and Potential Applications, Examples and Practical Projects

Lectures

Oral exam and written report focusing on the contents of the four parts of the course.

This course explores topics closely related to one or more goals of the United Nations 2030 Agenda for Sustainable Development (SDGs). 3 - Health and wellbeing 4- Quality education