EEG Source Localization: A Machine Learning Approach
Name
Gagandeep Singh -
Abstract
There are different techniques for recording human brain activity. One of them EEG can capture brain activity in the time frame at which the activity occurs, but has a poor spatial resolution. Another technology fMRI, captures brain activity with high spatial resolution compared to EEG, but with poor temporal resolution. Simultaneously recording brain activity using these two techniques helps us capture a richer, spatio-temporally more precise description of human brain activity. Inferring the source location within the brain from an EEG signal is defined as EEG source localization problem. In this thesis, a new method that is based on machine learning for solving EEG source localization problem is
proposed and its performance is evaluated on a simultaneously recorded EEG and fMRI
data set. This method’s performance is also compared to a commonly used method.
proposed and its performance is evaluated on a simultaneously recorded EEG and fMRI
data set. This method’s performance is also compared to a commonly used method.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Ilya Kuzovkin
Defence year
2018