Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
I sem. Oct 2, 2017 Jan 31, 2018
II sem. Mar 1, 2018 Jun 15, 2018
Exam sessions
Session From To
Sessione invernale d'esame Feb 1, 2018 Feb 28, 2018
Sessione estiva d'esame Jun 18, 2018 Jul 31, 2018
Sessione autunnale d'esame Sep 3, 2018 Sep 28, 2018
Degree sessions
Session From To
Sessione di laurea estiva Jul 18, 2018 Jul 18, 2018
Sessione di laurea autunnale Nov 22, 2018 Nov 22, 2018
Sessione di laurea invernale Mar 20, 2019 Mar 20, 2019
Period From To
Christmas break Dec 22, 2017 Jan 7, 2018
Easter break Mar 30, 2018 Apr 3, 2018
Patron Saint Day May 21, 2018 May 21, 2018
Vacanze estive Aug 6, 2018 Aug 19, 2018

Exam calendar

Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff


Baruffi Maria Caterina

Belussi Alberto +39 045 802 7980

Bombieri Nicola +39 045 802 7094

Bonacina Maria Paola +39 045 802 7046

Boscaini Maurizio

Busato Federico

Calanca Andrea +39 045 802 7847

Carra Damiano +39 045 802 7059

Castellini Alberto +39 045 802 7908

Combi Carlo 045 802 7985

Cristani Matteo 045 802 7983

Cristani Marco +39 045 802 7841

Daffara Claudia +39 045 802 7942

Dall'Alba Diego +39 045 802 7074

Di Pierro Alessandra +39 045 802 7971

Fiorini Paolo 045 802 7963

Fraccaroli Enrico 0458027048

Fummi Franco 045 802 7994

Geretti Luca +39 045 802 7850

Giachetti Andrea +39 045 8027998

Giacobazzi Roberto +39 045 802 7995

Gregorio Enrico 045 802 7937

Maris Bogdan Mihai +39 045 802 7074

Marzola Pasquina 045 802 7816 (ufficio); 045 802 7614 (laboratorio)

Mastroeni Isabella +39 045 802 7089

Migliorini Sara +39 045 802 7908

Muradore Riccardo +39 045 802 7835

Oliboni Barbara +39 045 802 7077

Posenato Roberto +39 045 802 7967

Pravadelli Graziano +39 045 802 7081

Quaglia Davide +39 045 802 7811

Quintarelli Elisa +39 045 802 7852

Segala Roberto 045 802 7997

Setti Francesco +39 045 802 7804

Spoto Nicola Fausto +39 045 8027940

Storti Silvia Francesca +39 045 802 7908

Tomazzoli Claudio

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.

One course to be chosen among the following
Final exam

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.

SPlacements in companies, public or private institutions and professional associations

Teaching code






Scientific Disciplinary Sector (SSD)


The teaching is organized as follows:





I semestre

Academic staff

Silvia Francesca Storti





I semestre

Academic staff

Silvia Francesca Storti

Learning outcomes

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.


MM: Theory
(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.

MM: Laboratory
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.


Reference texts
Activity Author Title Publishing house Year ISBN Notes
Teoria Maureen Clerc, Laurent Bougrain, Fabien Lotte Brain-Computer Interfaces 1: Foundations and Methods Wiley 2016 9781848218260
Teoria Luigi Landini Fondamenti di Analisi di Segnali Biomedici (con esercitazioni in Matlab) Pisa University Press 2013 9788867410965
Teoria . Materiale didattico fornito dal docente e disponibile su Moodle  

Examination Methods

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.

Type D and Type F activities

Modules not yet included

Career prospects

Module/Programme news

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.


List of theses and work experience proposals

theses proposals Research area
Analisi e percezione dei segnali biometrici per l'interazione con robot AI, Robotics & Automatic Control - AI, Robotics & Automatic Control
Integrazione del simulatore del robot Nao con Oculus Rift AI, Robotics & Automatic Control - AI, Robotics & Automatic Control
Domain Adaptation Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Computer graphics, computer vision, multi media, computer games
Domain Adaptation Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
BS or MS theses in automated reasoning Computing Methodologies - ARTIFICIAL INTELLIGENCE
Domain Adaptation Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION
Domain Adaptation Computing methodologies - Machine learning
Dati geografici Information Systems - INFORMATION SYSTEMS APPLICATIONS
Analisi e percezione dei segnali biometrici per l'interazione con robot Robotics - Robotics
Integrazione del simulatore del robot Nao con Oculus Rift Robotics - Robotics
BS or MS theses in automated reasoning Theory of computation - Logic
BS or MS theses in automated reasoning Theory of computation - Semantics and reasoning
Proposte di tesi/collaborazione/stage in Intelligenza Artificiale Applicata Various topics
Proposte di Tesi/Stage/Progetto nell'ambito delle basi di dati/sistemi informativi Various topics


As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.
Please refer to the Crisis Unit's latest updates for the mode of teaching.

Career management

Area riservata studenti