Neuroengineering

News:

20/02/2026 - The course website has been updated to academic year 2025-2026. The web page of the previous year is available here

The human brain is a complex learning system able to continuously process an enormous information flow and to translate it into actions with a time scale of milliseconds. As such, it has inspired many engineering solutions that are currently transforming the way we address problems at all levels and in all domains.

Aims

The aim of this course is to introduce students to the basics of the human brain functioning and organization at different scales, and to the main applications of Engineering and Information Technologies to Neuroscience. 

The learning objectives of the course will include:

• understanding the basics of the neural cells structure and functioning

• understanding how the propagation of electrical signals is used for cellular communication, and relating the properties of individual cells to their function in organized neural circuits and systems

• learning the basics of neural encoding and decoding 

• understanding the basis of network neuroscience

• acquiring knowledge of the principles, methodologies, and applications of the main engineering techniques used to study and interact with neural systems

• learning how to acquire, process and decode neurophysiological and muscular signals and how to interface them with external devices (brain-computer interfaces)

• meeting some examples of applications to neuroprosthetics and robot-assisted neurorehabilitation

Contents

Module A  (Prof. Laura Astolfi)

• Anatomy and physiology of the neural cell

• Generation of neural electrical and metabolic correlates

• Neural encoding and decoding

• Principles of the brain organization, natural neural networks, different levels of organization

• Network neuroscience - basic definitions (synchronicity, causality, influence)

• Model­-free (data driven) vs model­-based (biologically inspired) models of the brain as a complex system

• Analysis of brain networks at different scales (cellular and synaptic, cognitive neuroscience, behavioral neuroscience, multi-subject systems)

• Examples of application to clinical and physiological problems

Module B (Prof. Febo Cincotti)

• Non-invasive measurement of bioelectrical signals: electroencephalography (EEG), electromyography (EMG)

  • Overview of electrophysiological signals

  • Instrumentation for biosignals acquisition

  • Fundamentals of biosignal analysis and interpretation

  • Analysis of spontaneous, evoked and induced activity

• Basics of biosignal processing

  • Analog to digital conversion

  • Characterization of digital signals

  • Spectral analysis

  • Digital filters

• Brain-Computer Interfaces

Seminars

Experts in the field of neuroengineering will be invited to give seminars on methodological or applicative topics. The program is still to be defined. 

Possible topics: 

• On the use of Brain-Computer Interfaces in rehabilitation after brain stroke

• Study of the neural basis of social behavior and its pathological alterations

• ...

Prerequisites

The course is self-contained and does not need special prerequisites beyond those already required to access the curricula in which it is offered. Basic programming skills in any language (Python, Matlab, ...) will be needed to follow class demonstrations and complete homework. 

Course resources

Course mailing list

Class announcements and discussion will take place on a Piazza class (link).
The "class resources" section contains all links to other course resources and a pinned post contains instructions to access non-public material.
Students are incouraged to bookmark the Piazza class.

Shared folder

Teaching material will be available on a Google Drive shared folder.  This folder is not shared publicly, and you need to fill a form to gain access (see next paragraph).
N.B. Request made using the Google Drive "request access" button will not be acknowledged.

Access to teaching material

To request access to the course resources, please follow the instructions contained in the pinned post of the Piazza class (link). N.B. An institutional email address (@studenti.uniroma1.it) will be required.

Teaching material

Books

• Hari R, Puce A, MEG-EEG primer, Oxford Press,  2017, ISBN: 9780190497774 

• L.F. Dayan and D. Abbott, Theoretical Neuroscience. Computational and Mathematical Modeling of Neural Systems, the MIT Press, 2005. ISBN: 9780262041997 / 9780262541855

• M.X. Cohen, Analyzing Neural Time Series Data : Theory and Practice. The MIT Press, 2014 (available through the Sapienza Library System SBS)

• Wolpaw J and Wolpaw E (eds.), Brain-Computer Interfaces, Oxford University Press, 2012. ISBN 9780195388855 / 9780199921485

Handouts

Course notes and scientific articles will be distributed by the teachers during the semester.

Course schedule and timetable

The course will start on Tuesday 24 February 2026. Classes can only be taken in presence.

Lessons will take place at DIAG (via Ariosto 25) on:

• Tuesdays, 16:00-19:00 (Room B1)

• Wednesdays, 8:00-10:00 (Rooms A5/A6)

Join the Piazza class (link) to receive announcements and schedule changes.

Official timetables

• Control Engineering (2025-26)

• Artificial Intelligence and Robotics (2024-25)

Exams

Learning objectives

Knowledge and understanding. Students will learn the basics of the human brain functioning and organization at different scales, and to the main applications of engineering and information technologies to neuroscience

Applying knowledge and understanding. Students will familiarize with basic tools to utilize to acquire, process and decode neurophysiological and muscular signals and to interface them with artificial devices

Critical and judgment skills. Students will learn how to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution.

Communication skills. Students will learn to communicate in a multidisciplinary context the main issues of interfacing neurophysiological signals with artificial systems, and to convey possible design choices for this purpose.

Learning ability. Students will develop a mindset oriented to independent learning of advanced concepts not covered in the course.

Evaluation modalities

To pass the exam students are expected to:

• Pass a written test (closed-ended answers) on the day of the exam (Knowledge and understanding)

• Pass a written test (open-ended answers) on the day of the exam (Knowledge and understanding, Critical and judgment skills, Communication skills)

Examples of past exams are shared in the course resources.

Exams calendar

Exam dates are available on InfoStud and on the official Study Programme pages:

• Control Engineering exams calendar

• Artificial Intelligence and Robotics exams calendar

After the end of the registration period of each session, time and place of the exam will be communicated via email to the students who booked the exam.  Any information shared up to that point must be considered tentative.

Exams can only be booked via Infostud. This requires that you are regularly enrolled in the Study Programme and that you have already included Neuroengineering in your study plan.

Students are reminded that the October-November and the March-April sessions are reserved to students belonging to specific categories. including but not limited to "fuori corso" students. Please refer to the Students Regulation 2025-2026, Section 21.7, for a complete list (*).