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.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Master's Degree in Computer Engineering for Intelligent Systems - Enrollment from 2025/2026The 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 enrollment year.
1° Year
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2° Year activated in the A.Y. 2025/2026
| Modules | Credits | TAF | SSD |
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3 modules among the following
(A.A. 2025/2026 Internet of medical things not activated)| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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3 modules among the following
(A.A. 2025/2026 Internet of medical things not activated)| Modules | Credits | TAF | SSD |
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4 modules among the following:
- 1st year: Advanced visual computing and 3d modeling, Computer vision, Embedded & IoT systems design, Embedded operating systems, Robotics
- 2nd year: Advanced control systemsLegend | 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.
Advanced methods for biomedical signal processing (2025/2026)
Teaching code
4S012366
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING
Courses Single
Authorized
The teaching is organized as follows:
Teoria
Laboratorio
Learning objectives
The aim of this course is to equip students with a fundamental understanding of advanced methods and models used in biomedical signal processing, developing the ability to analyze and solve problems of significant relevance to the biomedical field. At the end of the course, students will be able to: •demonstrate a comprehensive knowledge of the advanced methods for biomedical signals processing; •use the acquired theoretical and practical knowledge to formulate, analyze and solve problems in bioengineering; •assess the traditional methods in the biomedical sciences in order to identify strengths and weaknesses. Overall, this course will equip students with the foundational knowledge and skills needed to effectively process, analyze, and interpret biomedical signals. It will also provide students with the ability to critically evaluate existing approaches and develop novel solutions to address real-world biomedical challenges.
Prerequisites and basic notions
(recommended) Basic knowledge of Fourier transforms, signal and system analysis in continuous and discrete time, probability and statistics (random variables, conditional probability), and Matlab programming.
Program
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MM: Teoria
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(1) Introduction to the main biomedical signals. Origin, characteristics and acquisition of the main bioelectric signals (EEG, MEG, ECG, EMG, evoked potentials, event-related potentials). Distinction between spontaneous and stimulus-related activity. (2) Common source of noise and artifacts in biomedical recordings. Techniques for signal denoising. Design and implementation of digital filters (FIR and IIR). (3) Deterministic and stochastic representations and proprieties of biomedical signals. Methods for the estimation of PSD: periodogram, Welch’s method, and model-based approaches (AR, MA, ARMA). Introduction of high-order spectra (e.g. bispectrum) and time-frequency analysis. (4) Methods for estimating functional and effective connectivity between brain regions using EEG/MEG data. Coherence, Granger causality, phase synchronization, and their applications in neuroscience. (5) Case study: brain-computer interfaces (BCIs). Overview of invasive and non-invasive recording techniques used in BCI applications. Description of the typical BCI pipeline: signal acquisition, preprocessing, feature extraction, classification, and feedback generation.
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MM: Laboratorio
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The course includes a series of laboratories in the computer lab with hands-on activities using MATLAB environment aimed at familiarizing students with the main analysis methods of biomedical signals (e.g. ECG, EMG, EEG, evoked potentials). The laboratories also comprise 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.
Bibliography
Didactic methods
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.
Learning assessment procedures
Assessment is conducted via oral examination preceded by a discussion on the group project assigned during the lab.
Evaluation criteria
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 biomedical signal processing.
Criteria for the composition of the final grade
The final grade will be the average of the two grades (2/3 theory, 1/3 lab).
Exam language
English
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