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Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment

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Abstract.

In this article we consider the application of parametric spectral analysis to multichannel event-related potentials (ERPs) during cognitive experiments. We show that with proper data preprocessing, Adaptive MultiVariate AutoRegressive (AMVAR) modeling is an effective technique for dealing with nonstationary ERP time series. We propose a bootstrap procedure to assess the variability in the estimated spectral quantities. Finally, we apply AMVAR spectral analysis to a visuomotor integration task, revealing rapidly changing cortical dynamics during different stages of task processing.

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Authors and Affiliations

  1. Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA, , , , , , US

    Mingzhou Ding, Steven L. Bressler, Weiming Yang & Hualou Liang

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  1. Mingzhou Ding
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  2. Steven L. Bressler
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  3. Weiming Yang
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  4. Hualou Liang
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Received: 20 August 1999 / Accepted in revised form: 17 December 1999

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Ding, M., Bressler, S., Yang, W. et al. Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol Cybern 83, 35–45 (2000). https://doi.org/10.1007/s004229900137

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  • DOI: https://doi.org/10.1007/s004229900137

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