Clustering Based Group-level Electromagnetic Spatiotemporal Independent Component Analysis
Date Issued
2009
Date
2009
Author(s)
Chang, Chia-Hao
Abstract
Electroencephalography (EEG) is a common technique for recording the electrical activity on the scalp generated by the activation of neurons within the brain. Independent components analysis (ICA) is widely used for eliminating noise and non-brain artifacts by decomposing EEG data into several independent components. Those components can also be used for estimating the activation of neurons. By clustering components across subjects, the common patterns of activations can be identified, which are useful for studying brain dynamics. Recently, a new variant of ICA, called Electromagnetic Spatiotemporal ICA (EMSICA), is proposed, which estimates spatiotemporal independent components and the activation of neurons simultaneously.n this thesis, EMSICA is applied for decomposing EEG data recorded from 9 participants in the experiment, and then the components are clustered according to the sources distributed on the whole cortical surface to find common patterns of activation. Traditional ICA is also applied to the same EEG data for comparison. In ICA, the source configuration for each independent component is represented by one or several equivalent dipoles. The results show that the source distribution estimated directly from EMSICA gives better estimation than equivalent dipoles. We also make some suggestions for improving the accuracy of clustering.
Subjects
EEG
Inverse problem
ICA
EMSICA
clustering
Type
thesis
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