CODE 106739 ACADEMIC YEAR 2023/2024 CREDITS 6 cfu anno 1 BIOENGINEERING 11159 (LM-21) - GENOVA 6 cfu anno 2 BIOENGINEERING 11159 (LM-21) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR ING-INF/06 LANGUAGE English TEACHING LOCATION GENOVA SEMESTER 2° Semester TEACHING MATERIALS AULAWEB OVERVIEW Brain signals play a central role in the field of neuroengineering. ThisThe course will cover fundamental methods for treating and interpreting brain signals at different scales (from the microscale of the single neuron up the macroscale of the whole brain). The students will understand how brain signals are generated, will be able to distinguish between different acquisition methodologies and will be provided all the tools to analyze, interpret and discuss brain data AIMS AND CONTENT LEARNING OUTCOMES The course aims to provide a critical analysis of the methods for analyzing the neuronal signal starting from the characterization of the single spike, to the activity of multiple cells up to the analysis of the electroencephalographic signal. The course will provide the basics to be able to manipulate, analyze and critically interpret the most common electrophysiological data AIMS AND LEARNING OUTCOMES Aim 1. Understanding the neurophysiological bases and generation mechanism of brain signals. Learning outcomes for Aim 1. The students will be able to describe how different types of neural signals are generated, at the different spatio-temporal scales (micro-meso-macro/large). They will be able to distinguish between different types of brain signals, also describing the recording methodologies typically used to acquire them. Aim 2. Extracting information from brain signals. Learning outcomes for Aim 2. The students will be able to identify optimal pre-processing steps, define feature extraction strategies based on real-case examples. They will be able to appropriately apply algorithms and methods, learn their implementation, optimization and pitfalls. Aim 3. Problem solving in real case examples of neural signal analysis. Learning outcomes for Aim 3. During the working groups, the students will acquire the capability to solve specific problems of data analysis by applying the techniques acquired during the course. The working groups will be organized such that different students will assume different roles as in real lab teams. PREREQUISITES Matlab programming, basic of neurophysiology, physics, linear algebra, TEACHING METHODS Lecture and flipped classes, problem-based learning, group work, blended learning SYLLABUS/CONTENT Micro-scale Introduction to neural signal analysis and applications (point process definition) Spike detection: definition, performance evaluation Spike sorting Spike Analysis (basic statistical properties and more advanced ones) Burst detection and Analysis Neural Avalanches Cross-correlation Connectivity Meso-scale Generating mechanisms for field potentials - from single neurons to neural ensembles Spectral feature analyses, separating oscillations from 1/f-like activity Volume conduction, signal leakage, ghost interactions Phase synchronization, amplitude correlation: how to separate phase from amplitude modulation, their interpretation - the communication through coherence framework Cross frequency coupling Large-scale From meso-to-macro scale recording: acquisition set up, physical basis and interpretations of electrical and magnetic field potentials The Electrical source imaging (ESI): forward and inverse solution for EEG Large-scale brain networks, their construction and characterization in the context of human brain mapping and connectomics Cortical travelling waves and neural avalanches The critical brain hypothesis RECOMMENDED READING/BIBLIOGRAPHY Analyzing neural time series - Cohen TEACHERS AND EXAM BOARD GABRIELE ARNULFO MICHELA CHIAPPALONE Ricevimento: MICHELA CHIAPPALONE. With appointment: Tel. 0103352991 or michela.chiappalone@unige.it Exam Board GABRIELE ARNULFO (President) VITTORIO SANGUINETI MICHELA CHIAPPALONE (President Substitute) LESSONS LESSONS START https://corsi.unige.it/11159/p/studenti-orario Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION The exam is composed of Project assignments (group work) and an Oral exam. Students will self-organise in small groups (max 3) and these groups will participate in several activities during the semester. Each assignment will be evaluated for its completeness and overall quality. ASSESSMENT METHODS Aim 1. Will be primarily assessed during oral examination where the students will be asked to critically discuss about the different methods presented Aim 2. and Aim 3. Will be assessed in the group-based assignments. Exam schedule Data appello Orario Luogo Degree type Note 19/01/2024 14:30 GENOVA Orale 13/02/2024 14:30 GENOVA Orale 27/05/2024 14:30 GENOVA Orale 17/06/2024 14:30 GENOVA Orale 15/07/2024 14:30 GENOVA Orale 30/08/2024 14:30 GENOVA Orale 12/09/2024 14:30 GENOVA Orale