Hidden Markov Independent Components Analysis for biosignal analysis

W. Penny, S. Roberts and R. Everson

Abstract

Much recent research in unsupervised learning builds on the idea of using generative models for modelling the probability distribution over a set of observations. These approaches suggest that powerful new data analysis tools may be derived by combining existing models use a probabilistic `generative' framework. In this paper we follow this approach and combine hidden Markov models (HMMs), Independent Component Analysis (ICA) and generalised autoregressive models (GAR) into a single generative model for the analysis of nonstationary multivariate time series

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