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

Luca Faes, Silvia Erla, Luca Faes, Giandomenico Nollo

Risultato della ricerca: Other

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
Pagine734-737
Numero di pagine4
Stato di pubblicazionePublished - 2009

Fingerprint

Least-Squares Analysis
Time series
Identification (control systems)

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Clinical Neurology
  • Neuroscience(all)

Cita questo

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

2009. 734-737.

Risultato della ricerca: Other

@conference{5fc8177c0d874691ab6cab7a7cb9c14d,
title = "Robust estimation of partial directed coherence by the vector optimal parameter search algorithm",
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.",
author = "Luca Faes and Silvia Erla and Luca Faes and Giandomenico Nollo",
year = "2009",
language = "English",
pages = "734--737",

}

TY - CONF

T1 - Robust estimation of partial directed coherence by the vector optimal parameter search algorithm

AU - Faes, Luca

AU - Erla, Silvia

AU - Faes, Luca

AU - Nollo, Giandomenico

PY - 2009

Y1 - 2009

N2 - 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.

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.

UR - http://hdl.handle.net/10447/278480

M3 - Other

SP - 734

EP - 737

ER -