Biomedical Engineering | Course catalogue

INDUSTRIAL NEUROSCIENCE

Course objectives

Knowledge and understanding: students will be able to understand the basics of the structure and functioning of the nerve cell; to link the activity of individual cells to their function within organised circuits and neuronal systems; to know the nature of the different correlates of brain activity, the techniques for their acquisition and the principles of analysis applied to them; to understand the concept of brain network or circuit, the different definitions of brain connectivity and the main techniques for its estimation and representation; to know the main engineering techniques used to study neuronal systems and interact with them; to know some examples of application to neuroprosthetics and robot-assisted neurorehabilitation. Applied knowledge and ability to understand: students will be able to choose the most suitable brain signal acquisition and analysis technique for the specific problem; to choose the brain network estimation method best suited to the nature of the data and to the design and clinical requirements; to choose how to acquire, process and decode brain signals and interface them with external, robotic devices, infrastructures and intelligent environments. Autonomy of judgement: students will be able to evaluate the implications and possible applications of the different acquisition and analysis methods studied to problems of a clinical, industrial and social nature. Communication skills: students will learn to communicate in a multidisciplinary context regarding the choices made in relation to the physiological or clinical problem addressed and to communicate and justify the choices made to this end. Learning skills: students will develop an independent learning attitude towards advanced concepts not covered in the course.

Channel 1

LAURA ASTOLFI Lecturers' profile

Program - Frequency - Exams

Course program

Introduction to neuroscience, principles of neuroanatomy and brain organisation Recalls of cellular electrophysiology: neuron structure, membrane potential, Nernst equation, transport mechanisms; action potential generation and propagation, graded potentials, synaptic transmission; information integration, temporal and spatial summation Generation of electrical correlates of brain activity Neuroelectrical correlates at different spatial scales (LFP, S-EEG, EcoG, scalp EEG) Scalp EEG: acquisition, limitations and advantages, qualitative analysis and brain rhythms Electroencephalographic rhythms EEG signal acquisition and pre-processing; introduction to artefacts Origin, recognition and treatment of EEG artefacts Definition of evoked and event-related potentials, segmentation and averaging, ERP generation hypothesis Representation of EP/ERP, early, intermediate and slow components, sensory potentials, P300, N400, Bereitschaftspotential and negative contingent variation Reactivity of EEG rhythms, definition of ERD and ERS, procedure for calculating ERD/ERS Examples of ERD/ERS, maps of ERD/ERS Spatial filters and Superficial Laplacian, surfaces for LS calculation, construction of realistic models Direct and inverse neuroelectrical problem Introduction to fMRI Characteristics of fMRI signal Applications of fMRI Other metabolic correlates of brain activity Comparison of brain signal acquisition methods Introduction to neuromodulation techniques Transcranial Magnetic Stimulation (TMS) Transcranial direct current electrical stimulation (tDCS) Introduction to multivariate analysis of brain signals Anatomical and functional connectivity Ordinary synchronism and coherence Physical and statistical causality Granger tests Multivariate methods for causality estimation Partial Directed Coherence Comparison of methods Introduction to graph theory in neuroscience Local, global indices, segregation measures of graphs Reference networks (regular, random, real, small-world graphs) Visualisation of networks Exercises on calculating graph indices Introduction to neurorehabilitation Brain plasticity: macroscopic mechanisms and neuronal mechanisms Motor rehabilitation: principles and bioengineering applications Cognitive rehabilitation Prosthetics, orthotics, activity and inclusion aids Brain signal decoding for environmental control, communication and rehabilitation

Prerequisites

No prerequisites.

Books

Hari R, Puce A, MEG-EEG primer, Oxford Press, 2017 M.X. Cohen, Analyzing Neural Time Series Data : Theory and Practice. The MIT Press, 2014 Wolpaw J and Wolpaw E (eds.), Brain-Computer Interfaces, Oxford University Press, 2012 Principi di stima dell'attività e della connettività cerebrale da dati neuroelettrici, Patron Editore Course notes, datasets and functions

Frequency

In presence.

Exam mode

Written exams for the evaluation of knowledge and understanding, applying knowledge and understanding and making judgements

Lesson mode

Teaching classes and exercises.