Time series analysis of hybrid neurophysiological data and application of mutual information
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Multivariate time series data of which some components are continuous time series and the rest are point processes are called hybrid data. Such data sets routinely arise while working with neuroscience data, EEG and spike trains would perhaps be the most obvious example. In this paper, we discuss the modeling of a hybrid time series, with the continuous component being the physiological tremors in the distal phalanx of the middle finger, and motor unit firings in the middle finger portion of the extensor digitorum communis (EDC) muscle. We employ a model for the two components based on Auto-regressive Moving Average (ARMA) type models. Another major issue to arise in the modeling of such data is to assess the goodness of fit. We suggest a visual procedure based on mutual information towards assessing the dependence pattern of hybrid data. The goodness of fit is also verified by standard model fitting diagnostic techniques for univariate data.
KeywordsHybrid data Generalized linear models ARMA models Cross-correlation Mutual information Maximum likelihood estimation
Professor J. R. Rosenberg kindly supplied the data. The authors also wish to thank an Associate Editor and two anonymous referees for their careful reading and some constructive suggestions which led considerable improvement over an earlier version of the manuscript.
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