Multitaper Spectral Granger Causality with Application to Ssvep

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceeding

Abstract

The traditional parametric approach to Granger causality (GC), based on linear vector autoregressive modeling, suffers from difficulties related to the inaccurate modeling of the generative process. These limits can be solved by using nonparametric spectral estimates in the frequency-domain formulation of GC, also known as spectral GC. In a simulation study, we compare the mean square error of the estimated spectral GC using different multitaper spectral estimators, finding that the Peak Matched multitapers are preferable for estimating spectral GC characterized by peaks. As an illustrative example, we apply the non-parametric approach to the analysis of brain functional connectivity in steady-state visually evoked potentials.

Detaljer

Författare
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Signalbehandling

Nyckelord

Originalspråkengelska
Titel på värdpublikationICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing
FörlagIEEE - Institute of Electrical and Electronics Engineers Inc.
Sidor1284-1288
ISBN (elektroniskt)978-1-5090-6631-5
StatusPublished - 2020 maj
PublikationskategoriForskning
Peer review utfördJa
EvenemangIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020 - Centre de Convencions Internacional de Barcelona (CCIB), Barcelona, Spanien
Varaktighet: 2020 maj 42020 maj 8
https://2020.ieeeicassp.org/

Konferens

KonferensIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020
Förkortad titelICASSP 2020
LandSpanien
OrtBarcelona
Period2020/05/042020/05/08
Internetadress