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Modeling the electroencephalogram by means of spatial spline smoothing and temporal autoregression

Biological Cybernetics volume 72, pages 249–259 (1995)Cite this article

Abstract

A spatial-temporal model for the description of electroencephalographic (EEG) data is introduced that combines smooth reconstruction in the spatial domain and autoregressive representation in the time domain. Its spatial aspect is formulated in a general framework that covers interpolation, smoothing, and regression. Contrary to the multivariate time series models used for EEG analysis up to date, the introduced model provides a smooth spatial reconstruction of the EEG cross-spectrum, keeping the condition of nonnegative definiteness. As an instance of practical importance, the case in which the spatial reconstruction is based on spherical splines is developed in detail. Illustrative examples are presented that show the flexibility of the model to describe both normal and abnormal EEG data.

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Affiliations

  1. Centro Nacional de Investigaciones Cientificas, POB 6880, Ciudad de la Habana, Cuba

    J. C. Jimenez, R. Biscay & O. Montoto

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  1. J. C. Jimenez
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  2. R. Biscay
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  3. O. Montoto
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Jimenez, J.C., Biscay, R. & Montoto, O. Modeling the electroencephalogram by means of spatial spline smoothing and temporal autoregression. Biol. Cybern. 72, 249–259 (1995). https://doi.org/10.1007/BF00201488

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  • DOI: https://doi.org/10.1007/BF00201488

Keywords

  • Time Series
  • General Framework
  • Spatial Domain
  • Practical Importance
  • Time Series Model