|CREDITS||6 credits during the 1st year of 11159 BIOENGINEERING (LM-21) GENOVA|
|SCIENTIFIC DISCIPLINARY SECTOR||ING-INF/06|
|TEACHING LOCATION||GENOVA (BIOENGINEERING)|
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
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
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.
Matlab programming, basic of neurophysiology, physics, linear algebra,
Lecture and flipped classes, problem-based learning, group work, blended learning
Introduction to neural signal analysis and applications (point process definition)
Spike detection: definition, performance evaluation
Spike Analysis (basic statistical properties and more advanced ones)
Burst detection and Analysis
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
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
Analyzing neural time series - Cohen
Office hours: On appointment
GABRIELE ARNULFO (President)
MICHELA CHIAPPALONE (President Substitute)
All class schedules are posted on the EasyAcademy portal.
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.
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.