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Decomposition of Neurological Multivariate Time Series by State Space Modelling

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Abstract

Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms. As a result, much more detailed decompositions become possible, and both components with sharp power spectrum, such as alpha components, sinusoidal artifacts, or sleep spindles, and with broad power spectrum, such as FMRI scanner artifacts or epileptic spiking components, can be separated, even in the absence of prior information. In addition, three generalizations are discussed, the first relaxing the independence assumption, the second introducing non-stationarity of the covariance of the noise driving the dynamics, and the third allowing for non-Gaussianity of the data through a non-linear observation function. Three application examples are presented, one electrocardigram time series and two electroencephalogram (EEG) time series. The two EEG examples, both from epilepsy patients, demonstrate the separation and removal of various artifacts, including hum noise and FMRI scanner artifacts, and the identification of sleep spindles, epileptic foci, and spiking components. Decompositions obtained by two ICA algorithms are shown for comparison.

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Authors and Affiliations

  1. Department of Neuropediatrics, University of Kiel, 24098, Kiel, Germany

    Andreas Galka, Hiltrud Muhle, Ulrich Stephani & Michael Siniatchkin

  2. Institute of Experimental and Applied Physics, University of Kiel, 24098, Kiel, Germany

    Andreas Galka

  3. Information and Coding Theory Lab, Faculty of Engineering, University of Kiel, 24098, Kiel, Germany

    Andreas Galka

  4. Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA

    Kin Foon Kevin Wong

  5. Tohoku University, 28 Kawauchi, Aoba-ku, Sendai, 980-8576, Japan

    Tohru Ozaki

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  1. Andreas Galka
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  2. Kin Foon Kevin Wong
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  3. Tohru Ozaki
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  4. Hiltrud Muhle
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  5. Ulrich Stephani
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Correspondence to Andreas Galka.

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Galka, A., Wong, K.F.K., Ozaki, T. et al. Decomposition of Neurological Multivariate Time Series by State Space Modelling. Bull Math Biol 73, 285–324 (2011). https://doi.org/10.1007/s11538-010-9563-y

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