Training and Research
PhD Programme Courses/classes - 2024/2025
Advanced techniques for acquisition of biomedical images
Credits: 1
Language: Ingelese
Teacher: Pietro Bontempi, Federico Boschi
Algorithmic motion planning in robotics
Credits: 1
Language: Italian
Teacher: Paolo Fiorini
Brain Computer Interfaces
Credits: 3
Language: Inglese
Teacher: Silvia Francesca Storti
Data visualization
Credits: 1
Language: Italian
Teacher: Andrea Giachetti
Modelli di Intelligenza Artificiale Spiegabile: stato dell'arte, promesse e sfide
Credits: 2,5
Language: Inglese
Teacher: Gloria Menegaz
Foundation of Robotics Autonomy
Credits: 1
Language: Italian
Teacher: Paolo Fiorini
Generative AI
Credits: 1,5
Language: English
Teacher: Francesco Setti
Modellazione e analisi 3D
Credits: 1
Language: Italian
Teacher: Andrea Giachetti
Modellazione e verifica di sistemi digitali
Credits: 1,5
Language: Italian
Teacher: Franco Fummi, Nicola Bombieri, Graziano Pravadelli
Nanomaterials: synthesis, characterization and applications
Credits: 1
Language: English
Teacher: Francesco Enrichi
Soft robotics: from nature to engineering
Credits: 1,5
Language: Italian
Teacher: Francesco Visentin
Techniques and algorithms for biomechanics of movement
Credits: 2,5
Language: English
Teacher: Roberto Di Marco
Theranostics: from materials to devices
Credits: 1
Language: Italian
Teacher: Nicola Daldosso
Brain Computer Interfaces (2024/2025)
Teacher
Referent
Credits
3
Language
Inglese
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
The recommended prerequisites of the course are basic familiarity with signal processing and programming in Matlab.
Program
- Introduction to the BCI model. Motivation for BCI. Its historical context and recent approaches. The BCI technology.
- Applications: in medicine, prevention of risk situations, smart environments, gaming etc.
- Invasive and non-invasive BCIs
- EEG-based control signals: evoked (e.g., SSVEP and P300 speller) vs. self-paced.
- Signal processing (filtering, feature extraction, classification) and the interpretation of the results.
- Kinesthetic motor imagery and introduction to a typical architecture of EEG-based MI-BCI (calibration and usage phases).
- The role of machine learning in BCIs.
- The classification problem and how to access performances.
- Case studies.
Laboratory. The lab involves implementing an MI-BCI interface in Matlab. Students will use EEGlab to create Matlab scripts and work on EEG-BCI data, filtering the data, extracting features like power spectral density, coherence, and correlation in the frequency bands of interest, implementing a classifier to distinguish different imagined movements. Finally, they will interpret the results obtained.
Bibliography
When and where
Borgo Roma, Ca’ Vignal, Room to be defined.
Tentative Schedule
• Tuesday, March 11, 2025
• Tuesday, March 18, 2025
• Tuesday, March 25, 2025
• Tuesday, April 1, 2025
• Tuesday, April 8, 2025
• Tuesday, April 15, 2025
Learning assessment procedures
The exam consists in developing a short project in Matlab for analyzing EEG-BCI data. This task will require students to apply the knowledge gained during the course, facing challenges related to processing and interpreting brain signals.
Assessment
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 EEG-based BCI signals; - they are able to program in MATLAB environment in the context of signal processing.
Criteria for the composition of the final grade
Pass/no pass.
Sustainable Development Goals - SDGs
This initiative contributes to the achievement of the Sustainable Development Goals of the UN Agenda 2030. More information on sustainabilityPhD school courses/classes - 2024/2025
PhD School training offer to be defined
Faculty
Fiorini Paolo
PhD students
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Guidelines for PhD students
1. Distribution of ECTS per Year (60 CFU/year)
16 credits for coursework are allocated for the 1st and 2nd year, and 8 credits for coursework in the 3rd year, divided equally (50%) between the PhD Program (Intelligent Systems Engineering) and the University of Verona's Doctoral School. The remaining credits are for research (44 in the 1st and 2nd year, and 52 in the 3rd year).
- Coursework ECTS of the PhD Program in Intelligent Systems Engineering: These are obtained by participating in the educational activities provided by the PhD Program in Intelligent Systems Engineering or by attending Winter Schools or Summer Schools. Participation in Winter and Summer Schools for the purpose of earning coursework ECTS must be agreed upon with the tutor and the Coordinator. The PhD program’s educational activities can be found in the "Educational Offer of the Program" section on the program's web pages.
- Coursework ECTS of the University of Verona's Doctoral School: These are obtained by attending seminars and transversal courses, which can be found in the "Educational Offer of the School" section on the Program’s web pages. This category includes activities provided in other competence areas of the School according to Ministry provisions, such as language, computer, and statistical courses, courses on library resources, copyright, and other topics related to the organization and management of research. Some of these activities may only require passing an assessment (without attending the course) to earn the ECTS.
- Research ECTS: These are obtained by working on the research project, participating in "optional" training activities both at the PhD Program site and elsewhere, attending conferences as a speaker or listener, or through publications, etc. The activities undertaken must be listed in the PhD student's annual report. The composition of the research ECTS is at the discretion of the PhD student and the tutor. Research ECTS do not need to be formally (self)certified or checked by the Coordinator, as they are evaluated by the Academic Board as part of the PhD student's annual report.
2. Research Stays Abroad
The PhD study regulations stipulate that "The PhD student usually undertakes periods of research, training, and internships at public or private entities abroad." For students in the PhD Program in Intelligent Systems Engineering, it is strongly recommended to carry out a research period abroad of at least three months, preferably between the second and third year, in a context conducive to developing the PhD project. Funding for missions abroad can be obtained through various Erasmus calls (for study and internship) and the UniVR mobility call, in addition to the annual budget allocated for each PhD student and any external funds.
3. Verification of Achievement of Educational Objectives
The achievement of educational objectives for advancing to the next year and for confirming the scholarship (for the 1st and 2nd year) or admission to the final exam (3rd year) is verified based on the following activities and documentation:
- Completed coursework credits module (checked by the Coordinator).
- End-of-year report on the activities carried out by the PhD student, experiences gained, and skills acquired (approved by the tutor).
- Presentation to a subcommittee including at least two members in addition to the tutor (and co-tutor) of the research results obtained during the year.
- Report from the abovementioned subcommittee on the research activity carried out during the year.
4. Forms
The forms can be found on the University’s Intranet in the section:
"How to → PhDs → My Career as a PhD Student"