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Time Series Analysis of Nonstationary Data in Encephalography and Related Noise Modelling

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Recent Advances in Mechatronics

Abstract

In this report, statistical time series analysis of nonstationary EEG/MEG data is proposed. The signal is investigated as a stochastic process, and approximated by a set of deterministic components contaminated by the noise which is modelled as a parametric autoregressive process. Separation of the deterministic part of time series from stochastic noise is obtained by an application of matching pursuit algorithm combined with testing for the residuum’s weak stationarity (in mean and in variance) after each iteration. The method is illustrated by an application to simulated nonstationary data.

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Authors and Affiliations

  1. Faculty of Fundamental Problems of Technology, Division of Biomedical and Measurement Engineering, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, Wrocław, 50-370, Poland

    L. Kipiński

Authors
  1. L. Kipiński
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Editors and Affiliations

  1. Faculty of Mechatronics, Warsaw University of Technology, Św. Andrzeja Boboli 8 street room 343, 02-525, Warsaw, Poland

    Ryszard Jabłoński , Mateusz Turkowski  & Roman Szewczyk ,  & 

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© 2007 Springer-Verlag Berlin Heidelberg

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Kipiński, L. (2007). Time Series Analysis of Nonstationary Data in Encephalography and Related Noise Modelling. In: Jabłoński, R., Turkowski, M., Szewczyk, R. (eds) Recent Advances in Mechatronics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73956-2_40

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  • DOI: https://doi.org/10.1007/978-3-540-73956-2_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73955-5

  • Online ISBN: 978-3-540-73956-2

  • eBook Packages: EngineeringEngineering (R0)

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