2024/2025
Mathematical Aspects of EEG and MEG Based Neuroimaging
Type:
Mago-Lego
Delivered by:
Institute for Cognitive Neuroscience
When:
3 module
Open to:
students of one campus
Language:
English
ECTS credits:
3
Course Syllabus
Abstract
The course “ Mathematical Aspects of EEG and MEG Based Neuroimaging” aims to introduce masters graduate students to basic theory of inverse modelling used to analyze the distribution of neuronal sources on the basis of EEG or MEG data. This shall prove to be useful for the students who are interested in learning mathematical aspects behind the process of converting non-invasively recor neuronal behavior and show the ways to motivate model choice as well as relations between the features of neuronal activity and dynamical properties of the models.ded data into the dynamic maps of neural activity. During the course we will briefly explore the forward model that describes the way the neuronal sources are mixed into sensor signals. The major portion of the class will be devoted to studying three classes of the approaches used to tackle the underdetermined inverse problem of EEG and MEG that lies in the heart of the transition from the sensor space to source space. The course can be considered as a deep dive into the engineering mathematics behind EEG and MEG based neuroimaging, one of the topics presented during the introductory “Neuroimaging techniques” class. We will start exploration of the inverse modelling from the classification of different types of approaches to reconstruction of neuronal sources from the multichannel EEG and MEG data. Then we will explore several representative solutions for the three main classes of these methods, will see how they behave when applied to modelled and real data, will learn the basic assumptions behind these methods and the effect of their parameters. The course provides students with the basic understanding of the inverse modelling philosophy in application to MEG and EEG, prepares them for comprehending modern methodological literature and attempts to build a landshaft for reasoning to support an educated choice of an inverse solver to apply in a specific study.