Topographic component (parallel factor) analysis of multichannel evoked potentials: practical issues in trilinear spatiotemporal decomposition.
We describe a substantive application of the trilinear topographic components/parallel factors model (TC/PARAFAC, due to Möcks/Harshman) to the decomposition of multichannel evoked potentials (MEP's). We provide practical guidelines and procedures for applying PARAFAC methodology to MEP decomposition. Specifically, we apply techniques of data preprocessing, orthogonality constraints, and validation of solutions in a complete TC analysis, for the first time using actual MEP data. The TC model is shown to be superior to the traditional bilinear principal components model in terms of data reduction, confirming the advantage of the TC model's added assumptions. The model is then shown to provide a unique spatiotemporal decomposition that is reproducible in different subject groups. The components are shown to be consistent with spatial/temporal features evident in the data, except for an artificial component resulting from latency jitter. Subject scores on this component are shown to reflect peak latencies in the data, suggesting a new aspect to statistical analyses based on subject scores. In general, the results support the conclusion that the TC model is a promising alternative to principal components for data reduction and analysis of MEP's.
Field
A S
AS
Department of Electrical Engineering and Computer Science, University of Illinois, Chicago 60680.
Graupe
D
D
D001931:Brain Mapping; D005071:Evoked Potentials; D005163:Factor Analysis, Statistical; D006801:Humans; D008959:Models, Neurological; D009626:Terminology as Topic