Course program
Section I: Statistics.
Introduction. Descriptive statistics. Probability distributions. Sampling distributions. Interval estimates. Inferential statistics. One-sample tests for means.
Independent-samples tests for means. Tests for more than two samples. Independent-samples analysis of variance (ANOVA).
Repeated-measures tests for means. Power of a test. Tests based on the Chi-square distribution.
Notes on non-parametric tests.
Section II: Introduction to classification.
Introduction. Metrics and performance evaluation procedures for a classifier. Parametric approaches. Data-driven approaches.
Non-recursive multilayer artificial neural networks. Overfitting.
Section III: Biosignal processing
Electromyographic signal processing. Review. Single-channel processing procedures.
Multi-channel acquisition from different muscle districts (muscle synergy, motor control), estimation of MUAPs and their functional interpretation, applications).
Neuroelectric source imaging. Review. Models for ERP estimation (classic and rephasing); Estimation of induced activity, ERD/S.
Multi-electrode recordings and spatial filters. Neuroelectric forward problem. Neuroelectric inverse problem.
Section IV: Computer lab sessions
Implementation of programs in the Matlab environment, applying the concepts learned in Sections I-III.
Prerequisites
Knowledge of the following is required:
● probability theory: single- and multi-dimensional random variables, probability functions and distributions, expected values, estimators;
● biosignal processing: physiology, instrumentation, and processing of electroencephalographic and electromyographic signals;
● programming notions and basic knowledge of the Matlab environment.
Books
The teaching material provided by the instructors includes:
● lecture slides
● lecture recordings
● past exam papers and solutions
● additional self-administered exercises to gain more familiarity with the Matlab language.
The material is shared on the cloud with all students of the course, and access procedures are described in the Piazza class (https://piazza.com/uniroma1.it/spring2026/1044421).
Frequency
Attendance is not mandatory. However, participation in laboratory sessions is recommended.
Exam mode
The assessment of preparation will be carried out through written and computer lab tests.
All tests are held in the same session, separated by a short break.
The written test will consist of:
(i) multiple-choice questions in which knowledge and understanding of the topics covered during the course are evaluated
(ii) an open-ended question, in which the depth of study of the subject and communication skills are evaluated.
In the lab test, problems will be proposed to be solved by writing scripts in the Matlab environment, in order to evaluate the ability to apply knowledge and understanding.
Bibliography
Lane, DM. Online Statistics Education: A Multimedia Course of Study, https://onlinestatbook.com
Surface Electromyography : Physiology, Engineering, and Applications; Editor(s):Roberto Merletti, Dario Farina, 2016, The Institute of Electrical and Electronics Engineers, Inc.
Lesson mode
Lectures (4 lessons/week) where program topics are explained and a selection of problems is solved.
Computer lab sessions (1/week) where students practice applying the learned concepts in the Matlab environment.
