Singular value decomposition--a general linear model for analysis of multivariate structure in the electroencephalogram

The application of Singular Value Decomposition (SVD) to analysis of EEG and evoked potential data has led to a hypothesis concerning the underlying structure of the EEG recorded from multiple channels. Based on the SVD algorithm the EEG is considered to be the linear combination of a sufficient number of features, each of which is defined in terms of its spatial distribution, temporal distribution, and amplitude. Use of this model leads to clear concepts concerning sampling, data reduction, normalization, and calculation of statistical significance, some of which are less evident when analysis is restricted to a single domain of interest.