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NEURAL SIGNAL ANALYSIS

CODE 106739
ACADEMIC YEAR 2022/2023
CREDITS
  • 6 cfu during the 1st year of 11159 BIOENGINEERING(LM-21) - GENOVA
  • 6 cfu during the 2nd year of 11159 BIOENGINEERING(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

    Il corso si prefigge l'obiettivo di fornire un'analisi critica dei metodi per l'analisi del segnale neuronale a partire dalla caratterizzazione del singolo spike, all'attività di più cellule fino all'analisi del segnale elettroencefalografico. Il corso fornirà le basi per poter manipolare, analizzare e interpretare criticamente i dati elettrofisiologici più comuni

    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 

    1.  Introduction to neural signal analysis and applications (point process definition)

    2. Spike detection: definition, performance evaluation

    3. Spike sorting

    4. Spike Analysis (basic statistical properties and more advanced ones)

    5. Burst detection and Analysis

    6. Neural Avalanches

    7. Cross-correlation

    8. Connectivity

    Meso-scale

    1. Generating mechanisms for field potentials - from single neurons to neural ensembles

    2. Spectral feature analyses, separating oscillations from 1/f-like activity

    3. Volume conduction, signal leakage, ghost interactions

    4. Phase synchronization, amplitude correlation: how to separate phase from amplitude modulation, their interpretation - the communication through coherence framework

    5. Cross frequency coupling 

    Large-scale

    1. From meso-to-macro scale recording: acquisition set up, physical basis and interpretations of electrical and magnetic field potentials

    2. The Electrical source imaging (ESI): forward and inverse solution for EEG

    3. Large-scale brain networks, their construction and characterization in the context of human brain mapping and connectomics

    4. Cortical travelling waves and neural avalanches 

    5. The critical brain hypothesis

    RECOMMENDED READING/BIBLIOGRAPHY

        Analyzing neural time series - Cohen 

     

    TEACHERS AND EXAM BOARD

    Exam Board

    GABRIELE ARNULFO (President)

    VITTORIO SANGUINETI

    MICHELA CHIAPPALONE (President Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    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

    Date Time Location Type Notes