Data Analysis in EEG and MEG
Hutt A.1, Jiang T.2, Kruggel F.3, Opitz B.1, Uhl C.1
1 Max-Planck-Institute of Cognitive Neuroscience, Leipzig, 2 National Laboratory of Pattern Recognition, Beijing, 3 University of California, Irvine
The analysis of electric and magnetic fields measured over the scalp surface is an important tool for gaining new insights into processes underlying phenomena such as cognitive processes, epileptic seizures, and sleep cycles. Basically, two complementary approaches are in use for investigating EEG/MEG data sets. First, spatial analyses focus on the determination of characteristic spatial distributions and their interpretation in terms of different brain functions. Secondly, temporal analyses aim at identifying characteristic temporal sequences, characterized as ERP components (e.g., N100, P300).
We tried to take a more holistic approach on analyzing the signal in a high-dimensional spatio-temporal space, and solved the problem of recording EEG during fMRI. Research in signal processing of EEG/MEG data focused on the following topics:
- to develop new methods for a spatio-temporal analysis of ERP data,
- to detect meta-stable states in the spatio-temporal signal, corresponding to meta-stable networks for information processing, and
- to solve technical and analytical problems in recording EEG during fMRI.
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