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

作者：

Highlights：

• Analytic expressions of five RQA measures for autoregressive processes are derived.

• Parametric RQA (pRQA) applies to time series modeled by autoregressive processes.

• pRQA is computationally fast and accurate.

• pRQA can detect spatial patterns in multichannel data, e.g. EEG data.

摘要

•Analytic expressions of five RQA measures for autoregressive processes are derived.•Parametric RQA (pRQA) applies to time series modeled by autoregressive processes.•pRQA is computationally fast and accurate.•pRQA can detect spatial patterns in multichannel data, e.g. EEG data.

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

论文评审过程：Received 21 July 2019, Revised 26 May 2020, Accepted 4 August 2020, Available online 5 August 2020, Version of Record 15 August 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107572