Training and Research
PhD Programme Courses/classes - 2023/2024
Non monotonic reasoning
Credits: 3
Language: English
Teacher: Matteo Cristani
Sustainable Embodied Mechanical Intelligence
Credits: 3
Language: English
Teacher: Giovanni Gerardo Muscolo
Brain Computer Interfaces
Credits: 3
Language: English
Teacher: Silvia Francesca Storti
Advanced Data Structures for Textual Data
Credits: 3
Language: English
Teacher: Zsuzsanna Liptak
AI and explainable models
Credits: 5
Language: English
Teacher: Gloria Menegaz, Lorenza Brusini
Automated Software Testing
Credits: 4
Language: English
Teacher: Mariano Ceccato
Autonomous Agents and Multi-Agent Systems
Credits: 5
Language: English
Teacher: Alessandro Farinelli
Cyber-physical systems security
Credits: 3
Language: English
Teacher: Massimo Merro
Elements of Machine Teaching: Theory and Appl.
Credits: 3
Language: English
Teacher: Ferdinando Cicalese
Fondamenti di Linguaggi Quantistici
Credits: 3
Language: English
Teacher: Margherita Zorzi
Introduction to Quantum Machine Learning
Credits: 4
Language: English
Teacher: Alessandra Di Pierro
Laboratory of quantum information in classical wave-optics analogy
Credits: 3
Language: English
Teacher: Claudia Daffara
Multimodal Learning and Applications
Credits: 5
Language: English
Teacher: Cigdem Beyan
Brain Computer Interfaces (2023/2024)
Teacher
Referent
Credits
3
Language
English
Class attendance
Free Choice
Location
VERONA
Learning objectives
The aim of this course is to propose an introduction to the basics of Brain Computer Interfaces (BCI) principally based on oscillatory EEG activity from a signal processing point of view. The course will introduce the main data processing methods that allow to decode brain activity in real time and convert it into a control signal for a BCI. In the first part the students will learn the following topics: the BCI model, the main BCI types with relative basic signal processing techniques for feature extraction and classification, the performance of the systems, the limitations of the current paradigms and the broad range BCI applications. The second part will cover practical BCI design and use, with an introduction to real-time processing of EEG recordings. Collaboration among students with different backgrounds will be encouraged through research-oriented practical group projects.
Prerequisites and basic notions
Course Area: Bioengineering/Neuroscience
Course Prerequisites: the recommended prerequisites of the course are basic familiarity with signal processing and programming in Matlab.
Program
Topics:
- Introduction to the BCI model and its historical context
- Invasive and non-invasive BCIs
- Evoked vs. self-paced BCIs
- Signal processing and the data interpretation (filtering, feature extraction, classification)
- The BCI technology
- Example of applications and how to access performances
- Applications and case studies
Laboratory. Data analysis: preprocessing (epoching and noise reduction), frequency-domain processing, train a support vector machine classifier to decode imagined movement of single trials, test classifier with cross-validation.
Bibliography
When and where
Schedule
Room: T.04 - Borgo Roma - Ca' Vignal 3 Time: 12:30-2:30 PM
• Monday, March 11, 2024
• Monday, March 18, 2024
• Monday, March 25, 2024
• Monday, April 08, 2024
• Monday, April 15, 2024
• Monday, April 22, 2024
Teaching methods. Regular lectures with power point presentation and blackboard, laboratory exercises and projects. The course adopts a "hands-on" approach, encouraging students to directly experience the design and implementation of the most suitable analysis methodologies to address real medical-clinical problems.
Learning assessment procedures
Assessment is conducted through project assigned during the lab sessions.
Assessment
-
Criteria for the composition of the final grade
-
Scheduled Lessons
| When | Classroom | Teacher | topics |
|---|---|---|---|
|
Monday 11 March 2024 12:30 - 14:30 Duration: 2:00 AM |
Ca' Vignal 3 - T.04 [04 - T] | Silvia Francesca Storti | Introduction to Brain-Computer Interfaces (BCIs): definition of BCI; how BCIs work; motivation for BCIs; the BCI model; the role of feedback; types of BCIs (active, reactive, and passive). Current neuroimaging-based BCI modalities. The role of machine learning in BCIs. Offline training and online testing. History of BCIs and recent approaches. |
|
Monday 18 March 2024 12:30 - 14:30 Duration: 2:00 AM |
Ca' Vignal 3 - T.04 [04 - T] | Silvia Francesca Storti | Applications of BCIs: medical applications (communication, rehabilitation and restoration, detection and diagnosis); prevention of risk situations (passive BCI), smart environments, neuromarketing, educational, gaming, military use. Design and implementation of BCIs. Signal acquisition methods (invasive and non-invasive BCIs). Focus on non-invasive EEG-based BCIs. |
|
Monday 25 March 2024 12:30 - 14:30 Duration: 2:00 AM |
Ca' Vignal 3 - T.04 [04 - T] | Silvia Francesca Storti | EEG-based BCI control signals: slow cortical potentials; evoked potentials (SSVEP, P300 speller), motor-imagery systems based on sensorimotor desynchronization. Kinesthetic motor imagery and introduction to a typical architecture of EEG-based MI-BCI (experimental paradigm, signal acquisition, signal preprocessing). |
|
Monday 08 April 2024 12:30 - 14:30 Duration: 2:00 AM |
Ca' Vignal 3 - T.04 [04 - T] | Silvia Francesca Storti | Study of a typical architecture of EEG-based MI-BCI: signal preprocessing methods for the removal of physiological and extraphysiological artifacts (temporal and spatial filtering), feature extraction methods based on spectral information (calibration phase), and time-frequency methods for online usage phase, event-related potentials (ERS/ERD), feature storage, the classification problem for MI-BCI systems (training data, predictor function, empirical risk, overfitting, and underfitting problems). |
|
Monday 15 April 2024 12:30 - 14:30 Duration: 2:00 AM |
Ca' Vignal 3 - T.04 [04 - T] | Silvia Francesca Storti | Laboratory. The laboratory involves implementing a simple offline MI-BCI interface in Matlab following the architecture explained during the lectures. The laboratory is structured into two main parts: preprocessing+feature extraction and classification. Initially, students receive a description of the experimental paradigm of the provided EEG data with an explanation of the key functions for scripting via EEGLAB (Matlab toolbox). Students uses a draft code and are required to implement some crucial processing steps. The features to extract are: power spectral density, coherence, and correlation for the alpha and beta frequency bands. |
PhD school courses/classes - 2023/2024
Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).
PhD students must obtain a specified number of CFUs each year by attending teaching activities offered by the PhD School.
First and second year students must obtain 8 CFUs. Teaching activities ex DM 226/2021 provide 5 CFUs; free choice activities provide 3 CFUs.
Third year students must obtain 4 CFUs. Teaching activities ex DM 226/2021 provide 2 CFUs; free choice activities provide 2 CFUs.
Registering for the courses is not required unless explicitly indicated; please consult the course information to verify whether registration is required or not. When registration is actually required, no confirmation e-mail will be sent after signing up.
Teaching Activities ex DM 226/2021: Linguistic Activities
INFORMATION: ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Law and Economics]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC WRITING SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC WRITING SKILLS [Law and Economics]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
Teaching Activities ex DM 226/2021: Research management and Enhancement
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Arts and Humanities]
Credits: 2,5
Language: Italian
Teacher: Donatella Boni
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Law and Economics]
Credits: 2,5
Language: Italian
Teacher: Luisella Zocca
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Scientific Area]
Credits: 2,5
Language: Italian
Teacher: Elena Scanferla
Teaching Activities ex DM 226/2021: Statistics and Computer Sciences
INTRODUCTION TO PROBABILITY (MODULE I)
Credits: 1
Language: English
Teacher: Marco Minozzo
INTRODUCTION TO PROBABILITY (MODULE II)
Credits: 1
Language: English
Teacher: Marco Minozzo
BASIC LEVEL STATISTICS
Credits: 2,5
Language: English
INTRODUCTION TO STATISTICAL INFERENCE
Credits: 1
Language: English
Validità e affidabilità delle misure e dei test diagnostici
Credits: 0,5
Language: English
Teacher: Alessandro Marcon
BASIC LEVEL STATISTICS
Credits: 2,5
Language: Italian
Statistical analysis with R - module I
Credits: 1
Language: Italian
Teacher: Erica Secchettin
Generalized linear models: logistic regression, loglinear model, Poisson model
Credits: 2
Language: English
Teacher: Lucia Cazzoletti
Disegno dello studio nella ricerca osservazionale e sperimentale
Credits: 1,5
Language: English
Teacher: Alessandro Marcon
Calcolo della numerosità campionaria in funzione di una precisione o potenza statistica prefissata
Credits: 1
Language: English
Teacher: Giuseppe Verlato
Introduzione alla meta-analisi per la ricerca biomedica (revisione della letteratura, raccolta dei dati, costruzione del database)
Credits: 1
Language: English
Teacher: Giuseppe Verlato
Applicazioni della meta-analisi in campo epidemiologico e medico
Credits: 1
Language: English
Teacher: Giuseppe Verlato
Analisi di sopravvivenza: test log-rank, curve di sopravvivenza di Kaplan-Meier, modello di regressione di Cox
Credits: 1,5
Language: English
Teacher: Simone Accordini
INTERMEDIATE STATISTICS [Recommended for Human Sciences]
Credits: 2,5
Language: English
INTERMEDIATE STATISTICS [Tutti i corsi di studio]
Credits: 2,5
Language: English
Statistical analysis with R - module II
Credits: 2
Language: Italian
Teacher: Erica Secchettin
Teaching Activities: Free choice
PROTECTING PSYCHOLOGICAL WELL-BEING IN THE PHD PROGRAM: WHAT DO WE NEED TO CONSIDER FOR BEING A GOOD SCIENTIST: BEST PRACTICE AND THE ETHICS OF SCIENCE
Credits: 1
Language: inglese
Teacher: Paola Cesari
QUANDO LA RICERCA SI FA ETICA (PERCORSO ORGANIZZATO E FINANZIATO DAL TEACHING AND LEARNING CENTER DI UNIVR)
Credits: 2
Language: Italian
Teacher: Roberta Silva
IMPARA IL MARKETING DIGITALE
Credits: 1,5
Language: English
Italian Poetry abroad
Credits: 1
Language: Italiano
Teacher: Massimo Natale
COSTRUISCI IL TUO BUSINESS MODEL CANVAS
Credits: 1,5
Language: English
APPROCCI E METODOLOGIE PARTECIPATIVE NELLA RICERCA CON GLI ATTORI DEL TERRITORIO
Credits: 1,5
Language: Italian
Teacher: Cristiana Zara
DOING INTERVIEWS IN QUALITATIVE RESEARCH
Credits: 1,5
Language: English
Teacher: Chiara Sità
LA COMUNICAZIONE UMANISTICA: OPPORTUNITA' E RISCHI
Credits: 1
Language: Italiano
DIFFERENTIAL DIAGNOSIS OF DEMYELINATING DISEASES OF THE CENTRAL NERVOUS SYSTEM
Credits: 2
Language: English
Teacher: Alberto Gajofatto
IL SONNO E I SUOI DISTURBI: FOCUS SULLE PARASONNIE E I DISTURBI DEL MOVIMENTO IN SONNO
Credits: 1
Language: English
Teacher: Elena Antelmi
IMAGING TECHNIQUES FOR BODY COMPOSITION ANALYSIS
Credits: 1
Language: English
Teacher: Carlo Zancanaro
OPEN SCIENCE: THE MIGHTY STICK AGAINST "BAD" SCIENCE
Credits: 2
Language: English
Teacher: Alberto Scandola
THE EMPIRICAL PHENOMENOLOGICAL METHOD (EPM): THEORETICAL FOUNDATION AND EMPIRICAL APPLICATION IN EDUCATIONAL AND HEALTHCARE FIELDS
Credits: 2
Language: English
THE PATHWAY OF OXYGEN: CAUSE OF HYPOXEMIA
Credits: 1
Language: English
Teacher: Carlo Capelli
Faculty
+39 045 802 7980
cigdem.beyan@univr.it
mariano.ceccato@univr.it
mila.dallapreda@univr.it
giovannigerardo.muscolo@univr.it
PhD students
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Guidelines for PhD students
Below you will find the files that contain the Guidelines for PhD students and rules for the acquisition of ECTS credits (in Italian: "CFU") for the Academic Year 2023/2024.
Documents
| Title | Info File |
|---|---|
Dottorandi: linee guida generali (2023/2024)
|
pdf, it, 93 KB, 26/02/24 |
|
PhD students: general guidelines (2023/2024)
|
pdf, en, 94 KB, 26/02/24 |
