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

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