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A common class of methods for analyzing of multivariate time series,stationary and nonstationary, decomposes the observed series into latentsources. Methods such as principal compoment analysis (PCA), independentcomponent analysis (ICA) and Stationary Subspace Analysis (SSA) assume theobserved multivariate process is generated by latent sources that arestationary or nonstationary. We develop a method that tracks changes in thecomplexity of a 32-channel local field potential (LFP) signal from a ratfollowing an experimentally induced stroke. We study complexity through thelatent sources and their dimensions that can change across epochs due to aninduced shock to the cortical system. Our method compares the spread ofspectral information in several multivariate stationary processes withdifferent dimensions. A frequency specific spectral ratio (FS-ratio) statisticis proposed and its asymptotic properties are derived. The FS-ratio is blind tothe dimension of the stationary process and captures the proportion of spectralinformation in various (user-specified) frequency bands. We apply our method tostudy differences in complexity and structure of the LFP before and aftersystem shock. The analysis indicates that spectral information in the betafrequency band (12-30 Hertz) demonstrated the greatest change in structure andcomplexity due to the stroke.

Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals