I am delighted to announce the following talk by Aapo Hyvarinen, which will take place on Thursday June 23rd at 14h30. The talk will be held at the University of Montreal, with the exact room to be announced at a later date. Hope to see you there !
Title: Unsupervised machine learning for analysis of EEG and MEG at rest Speaker: Aapo Hyvarinen
Abstract: Recently, a lot of brain imaging has concentrated on analyzing brain activity at rest, i.e. when the subject is not doing anything particular and does not receive specific stimulation. When using functional magnetic resonance imaging (fMRI), such analysis is typically done by independent component analysis (ICA). However, there has not been very much work on analysing resting activity measured by EEG or MEG. We have recently developed various methods to analyse EEG/MEG data at rest. First, we have created new variants of ICA to more completely exploit the statistical structure of EEG/MEG data. Second, we have developed tests of the statistical significance of the independent components. Third, we have a framework for analysis of causality (connectivity) which uses the non-Gaussianity of the data and hopefully it can be used to analyse the connectivity of the independent components.