A Multivariate Analysis of Evoked Responses in EEG and MEG Data

This paper presents a multivariate analysis of evoked responses and their spatiotemporal dynamics as measured with electro- or magnetoencephalography. This analysis uses standard techniques (ManCova) to make possible statistical inference about differential responses, after the data have been transformed using singular value decomposition. The generality of this approach is limited only by the assumptions implicit in the general linear model and can range from simple analyses like Hotelling'sT2test (in comparing evoked responses among different conditions) to complex analyses of a multivariate regression type (e.g., characterizing the response components associated with a behavioral or psychophysical parameter). To illustrate the technique we have characterized time-dependent changes (both within and between trials) in magnetic fields, evoked by self-paced movements. Our illustrative analysis showed that movement-evoked components were less prone to adaptation than premovement components, suggesting that functionally distinct (preparatory and early executive) biomagnetic signals show differential adaptation.