Parametric recurrence quantification analysis of autoregressive processes for pattern recognition in multichannel electroencephalographic data

Sofiane Ramdani, Anthony Boyer, Stéphane Caron, François Bonnetblanc, Frédéric Bouchara, HuguesDuffau and Annick Lesne. Pattern Recognition. Submitted July 2019. Accepted August 2020. To appear in January 2021.

Abstract

Recurrence quantification analysis (RQA) is an acknowledged method for the characterization of experimental time series. We propose a parametric version of RQA, pRQA, allowing a fast processing of spatial arrays of time series, once each is modeled by an autoregressive stochastic process. This method relies on the analytical derivation of asymptotic expressions for five current RQA measures as a function of the model parameters. By avoiding the construction of the recurrence plot of the time series, pRQA is computationally efficient. As a proof of principle, we apply pRQA to pattern recognition in multichannel electroencephalographic (EEG) data from a patient with a brain tumor.

BibTeX

@article{ramdani2021patcog,
  title = {Parametric recurrence quantification analysis of autoregressive processes for pattern recognition in multichannel electroencephalographic data},
  author = {Ramdani, Sofiane and Boyer, Anthony and Caron, St{\'e}phane and Bonnetblanc, Fran{\c{c}}ois and Bouchara, Frederic and Duffau, Hugues and Lesne, Annick},
  journal = {Pattern Recognition},
  pages = {107572},
  year = {2021},
  month = jan,
  volume = {109},
  publisher = {Elsevier},
  doi = {10.1016/j.patcog.2020.107572},
}
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