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

PATTERN RECOGNITION

卷:109

Article

Ramdani, Sofiane¹  Boyer, Anthony²  Caron, Stephane¹  Bonnetblanc, Francois^2,3  Bouchara, Frederic⁴  Duffau, Hugues^5,6  Lesne, Annick^7,8 

[1] Univ Montpellier, CNRS, IDH, LIRMM, Montpellier, France

[2] Univ Montpellier, CAMIN, INRIA, LIRMM, Montpellier, France

[3] Univ Bourgogne Franche Comte, INSERM U1093, UFR STAPS, Cognit Act & Plasticite Sensorimotrice, F-21078 Dijon, France

[4] Aix Marseille Univ, Univ Toulon, CNRS, LIS, Toulon, France

[5] INSERM U1051, Plasticite Cerebrate Cellules Souches Neurales &, F-34295 Montpellier, France

[6] Inst Neurosci Montpellier, F-34295 Montpellier, France

[7] Sorbonne Univ, CNRS, Lab Phys Theor Matiere Condensee, LPTMC, F-75252 Paris, France

[8] Univ Montpellier, CNRS, Inst Genet Mol Montpellier, Montpellier, France

关键词: Recurrence plots;    Recurrence quantification analysis;    Autoregressive stochastic processes;    Asymptotic recurrence measures;    Multichannel data;    EEG Data;   

DOI  :  10.1016/j.patcog.2020.107572

来源: Elsevier

【 摘 要 】

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. (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

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