Training and Research
PhD Programme Courses/classes
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
A practical interdisciplinary PhD course on exploratory data analysis
Credits: 4
Language: English
Teacher: Prof. Vincenzo Bonnici (Università di Parma)
Multimodal Learning and Applications
Credits: 5
Language: English
Teacher: Cigdem Beyan
Introduction to Blockchain
Credits: 3
Language: English
Teacher: Sara Migliorini
Autonomous Agents and Multi-Agent Systems
Credits: 5
Language: English
Teacher: Alessandro Farinelli
Cyber-physical systems security
Credits: 3
Language: English/Italian
Teacher: Massimo Merro
Foundations of quantum languages
Credits: 3
Language: English
Teacher: Margherita Zorzi
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
Elements of Machine Teaching: Theory and Appl.
Credits: 3
Language: English
Teacher: Ferdinando Cicalese
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
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
Didactic methods
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. |
|
Monday 22 April 2024 12:30 - 14:30 Duration: 2:00 AM |
Ca' Vignal 3 - T.04 [04 - T] | Silvia Francesca Storti | Laboratory. This second part involves training a support vector machine (SVM) classifier to decode imagined movement of single trials and testing the classifier with cross-validation. Explanation is provided on how to assess the classifier's performance and optimize the algorithm's hyperparameters. The main challenges in BCI classification are highlighted: insufficient quantity of data, poor data quality, non-representative training data, irrelevant features requiring dimensionality reduction (channel/feature selection). Model evaluation involves considerations such as overfitting and underfitting the training data. In this part as well, students use a code template and are tasked with implementing crucial processing steps, focusing on the interpretation of the obtained results. As a small project, students are assigned the processing of new EEG-BCI data. |
Sustainable Development Goals - SDGs
This initiative contributes to the achievement of the Sustainable Development Goals of the UN Agenda 2030. More information on sustainability
