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CODE 106739
ACADEMIC YEAR 2023/2024
CREDITS
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 

  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

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 Ora 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
10/06/2024 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