Dept of Mathematics and Statistics & Dept of Computer Science
University of Helsinki
Thursday 9 September 2010
Seminar Room B10 (Basement)
Alexandra House, 17 Queen Square, London, WC1N 3AR
Advances in the analysis of spontaneous EEG and MEG by independent component analysis.
Analysis of spontaneous brain activity, for example when the subject is simply resting, has recently become an important topic. While a lot of progress has been made in the context of fMRI, such analysis is still rare for EEG and MEG. Since there is no input to the system, unsupervised methods are a natural approach to analyze activity at rest. In this talk, I will describe some new methods for EEG/MEG analysis, centered around independent component analysis (ICA). I will first argue that one reason why resting-state analysis is not straightforward in EEG and MEG is that the time-frequency structure of the data should be taken into account. I will also introduce a spatial variant of ICA on MEG which is more similar to the way ICA is usually applied in the context of fMRI. Next, I will propose a new method for testing which independent components are statistically significant, something which has not been really done before. At this point, I will have run out of time, so I will only very briefly describe some ideas on analyzing connectivity between the components obtained.