Neuroengineering year 2019-2020

(archived page)

Course code: 10592834

ECTS credits: 6 credits (60 hours of classes, 5 hours/week)

Period: second semester (end of February - end of May)

Offered in the degree programs:

  • MSc in Artificial Intelligence and Robotics (2019-20)

  • MSc in Control Engineering (2019-20)

News:

09/03/2020 - Classes are resumed in online modality. Check the Piazza class for more information.

05/03/2020 - Classes are suspended due the decree regarding the containment of the COVID-19 outbreak.

25/02/2020 - Classes of the first week will be held in Room B2

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 I (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 II (Prof. Febo Cincotti)

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

    • Characterization of the signals

    • Fundamentals of biosignal interpretation

    • Practical experience with acquisition hardware and analysis software

  • Brain-Computer Interfaces

    • Real-time biosignal processing, feature extraction and translation

    • Applications in the neurorehabilitation domain

    • Practical experience and projects

  • Stimulation of the human nervous and muscular systems: Transcranial Magnetic Stimulation (TMS), Functional Electrical Stimulation (FES)

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

  • Restoring motor and somatosensory functions through neural robotic prostheses

  • 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 complete the course projects.

Teaching material

Books

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

  • 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)

Handouts

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

Course resources

Course mailing list

Class communications and discussion will take place on a Piazza class.

Shared folder

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

Access to resources

To request access to the course resources, please fill this form. N.B. An institutional email address (@studenti.uniroma1.it) is required.

Course schedule and timetable

Note that from 9 March until the cease of effect of the decree that prevents in-person teaching to contrast the outbreak of COVID-19, all classes will be held online. Check the Piazza forum for details.

In the academic year 2019-2020, lessons will take place on:

  • Wednesdays, 8:00-11:00 (Room A2 B2, DIAG)

  • Fridays, 14:00-16:00 (RoomA2 B2, DIAG)

(last update: 09/03/2020)

Official timetables

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 with full grade students are expected to:

  • Participate in the class activities (lectures, discussions, hands-on laboratories) (Communication skills)

  • Deliver a course project (Applying knowledge and understanding, Learning ability)

  • Take a written test on the day of the exam (Knowledge and understanding, Critical and judgment skills)

  • Submit and discuss a 4-page report about the selected project (Communication skills, Critical and judgment skills)


Exams calendar (academic year 2019-2020)

(last updated 20/11/2019. Always double check on Infostud for updates not mirrored here.)

Exam dates are available on InfoStud and listed below for convenience. Hours and room of the exam will be communicated to the registered students after the end of the registration period (any information before then must be considered tentative, and checked for confirmation 2-3 days before the exams).

  • Session III:

    • exam date: 09/06/2020

    • registration from 12/05/2019 to 03/06/2020

  • Session IV:

    • exam date: 07/07/2020

    • registration from 09/06/2019 to 01/07/2020

  • Session V:

    • exam date: 17/09/2020

    • registration from 30/07/2020 to 11/09/2020

  • Reserved Session II (*):

    • exam date: TBD (between 05/10/2020 and 5/11/2020)

(*) reserved to specific student categories, please consult your Educational Affairs Office.

Contacts

Prof. Laura Astolfi (🌐, ✉)

Prof. Febo Cincotti (🌐, ✉)