The aim of this course is to provide the basic knowledge of methods and models for biomedical signal and image processing. At the end of the course, the student will be able to show knowledge of the main methods of biomedical signal and image processing and will possess the ability to understand advanced topics in bioengineering, will be able to analyze and solve problems of interest in the field of bioengineering through the acquired tools, both theoretical and practical; will to be able to autonomously continue studies in the field of bioengineering.

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MM: Theory
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(1) Main biomedical signals and images. Origin, characteristics and acquisition of the main bioelectric signals (electroencephalographic signal - EEG, magnetoencephalographic – MEG, electrocardiographic - ECG, electromyographic - EMG, spontaneous and induced signals, evoked potentials - EP, event-related potentials - ERP); introduction to bioimaging.

(2) Analysis techniques in the time and frequency domains. Fundamentals of digital signal processing and characterization in the time domain. Digital filtering methods, sampling, A/D conversion, waveform recognition algorithms. Classic methods for frequency analysis; frequency bands and power spectrum, periodogram; time/frequency resolution; bispectra and coherence; feature extraction methods. Brain source imaging (direct and inverse problems for EEG and MEG signals) and functional and effective connectivity analysis methods. Applications on in-silico and real signals.

(3) Statistical analysis of biomedical data. Review of basic concepts of descriptive and inferential statistics. Description of the measurement error, statistical description of the experimental data: statistical indices, confidence intervals, hypothesis test and significance level, simple and multivariate linear regression for biomedical signals and images.

(4) Brain-computer interfaces. Introduction to the main data processing methods that allow to decode brain activity in real time and convert it into a control signal for a brain-computer interface. We will discuss the BCI model and its historical context, the invasive and non-invasive techniques allowing to measure in real time the responses of an individual to particular stimuli, the data interpretation (filtering, future extraction, classification) and the BCI technology.

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MM: Laboratory
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The course includes a series of laboratories in the computer lab with hands-on activities mainly in MATLAB environment aimed at familiarizing students with the main analysis methods of biomedical signals and images (e.g. ECG, EMG, EEG, evoked potentials, functional magnetic resonance imaging - fMRI). The laboratories also foresee a project activity in small groups for the solution of problems related to the analysis of biomedical data. The laboratories complement lectures by consolidating learning and developing problem-solving and hands-on practical skills in the context of bioengineering.

Teaching methods. Regular lectures with power point presentation and blackboard, laboratory exercises and projects. The course approach is "hands on" where students will experiment the design and data analysis with the most suitable methodologies to solve real-life clinical-medical problems. Educational material will be available to students enrolled in the course on the Moodle platform. This material includes lecture presentations in PDF format and material related to laboratory activities. For further details and supplementary materials, please refer to the reference books.

Assessment is conducted via oral examination preceded by a discussion on the group project assigned during the lab. The final grade will be the average of the two grades (2/3 theory, 1/3 lab).
To pass the exam, the students must show that:
- they have understood the theoretical and practical concepts of the course;
- they are able to use the knowledge acquired during the course to solve the assigned problems related to the processing of biomedical signals and data;
- they are able to program in MATLAB environment in the context of signal and biomedical data processing.