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

Luca Faes, Silvia Erla, Luca Faes, Giandomenico Nollo

Risultato della ricerca: Otherpeer review

1 Citazioni (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

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Clinical Neurology
  • ???subjectarea.asjc.2800.2800???

Fingerprint Entra nei temi di ricerca di 'Robust estimation of partial directed coherence by the vector optimal parameter search algorithm'. Insieme formano una fingerprint unica.

Cita questo