Anwendung trilinearer Komponentenanalyse auf die Analyse ereignisbezogener EEG-Potentiale

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As recorded at the scalp, event-related EEG potentials (ERPs) consist of several overlapping components. To estimate parameters of these components, Principal Component Analysis (PCA) was recommended as the method of choice 25 years ago. However, PCA cannot arrive at a unique decomposition of the data without imposing additional constraints (nor can PCA s younger sibling , ICA). These constraints are usually formal (not model-driven), e.g., that some component should be focused on as few variables as possible (Varimax and Promax rotations), which does not necessarily lead to a decomposition that makes sense in physiological terms. Möcks (1988a, 1988b, 1991) suggested the model that a component should be characterized by its topographical distribution across recording sites being identical across observations. He proved that this leads to unique model-driven solutions and he developed an algorithm that reaches convergence. This Trilinear Components Analysis will here be applied to ERP analysis systematically for the first time. To reach this goal, the mathematical core of the present algorithm has to be tested, and alternative algorithmic options have to be realized. The critical test for application will be realized in an already available data set that has large experimental variance where we will look for an answer on the question how much the P3 component evoked by 82 In an 81-82 task overlaps with the downswinging overshoot ofthe preceding CNV.