Robust estimation of partial directed coherence by the vector optimal parameter search algorithm

Luca Faes, Giandomenico Nollo, Silvia Erla

Risultato della ricerca: Paper

1 Citazione (Scopus)

Abstract

We propose a method for the accurate estimation of Partial Directed Coherence (PDC) from multichannel time series. The method is based on multivariate vector autoregressive (MVAR) model identification performed through the recently proposed Vector Optimal Parameter Search (VOPS) algorithm. Using Monte Carlo simulations generated by different MVAR models, the proposed VOPS algorithm is compared with the traditional Vector Least Squares (VLS) identification method. We show that the VOPS provides more accurate PDC estimates than the VLS (either overall and single-arc errors) in presence of interactions with long delays and missing terms, and for noisy multichannel time series. ©2009 IEEE.
Lingua originaleEnglish
Stato di pubblicazionePublished - 2009

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Least-Squares Analysis
Time series
Identification (control systems)

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Neuroscience(all)
  • Clinical Neurology

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Robust estimation of partial directed coherence by the vector optimal parameter search algorithm. / Faes, Luca; Nollo, Giandomenico; Erla, Silvia.

2009.

Risultato della ricerca: Paper

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abstract = "We propose a method for the accurate estimation of Partial Directed Coherence (PDC) from multichannel time series. The method is based on multivariate vector autoregressive (MVAR) model identification performed through the recently proposed Vector Optimal Parameter Search (VOPS) algorithm. Using Monte Carlo simulations generated by different MVAR models, the proposed VOPS algorithm is compared with the traditional Vector Least Squares (VLS) identification method. We show that the VOPS provides more accurate PDC estimates than the VLS (either overall and single-arc errors) in presence of interactions with long delays and missing terms, and for noisy multichannel time series. {\^A}{\circledC}2009 IEEE.",
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AB - We propose a method for the accurate estimation of Partial Directed Coherence (PDC) from multichannel time series. The method is based on multivariate vector autoregressive (MVAR) model identification performed through the recently proposed Vector Optimal Parameter Search (VOPS) algorithm. Using Monte Carlo simulations generated by different MVAR models, the proposed VOPS algorithm is compared with the traditional Vector Least Squares (VLS) identification method. We show that the VOPS provides more accurate PDC estimates than the VLS (either overall and single-arc errors) in presence of interactions with long delays and missing terms, and for noisy multichannel time series. ©2009 IEEE.

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