Abstract
This contribution describes the application of topographic independent component analysis to fMRI signal analysis. This new discriminating paradigm represents a combination of signal separation based on traditional independent component analysis with at the same time clustering based on a topographic order. When applied to fMRI analysis, this new method outperforms both traditional independent component analysis as well as other standard clustering techniques.
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