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Multitaper Spectral Granger Causality with Application to Ssvep

Anderson, Rachele LU orcid and Sandsten, Maria LU (2020) IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020 p.1284-1288
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.
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Non-parametric spectral Granger causality, multitaper spectral estimation, functional connectivity, steady-state visually evoked potentials (SSVEP), EEG
host publication
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing
pages
1284 - 1288
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020
conference location
Barcelona, Spain
conference dates
2020-05-04 - 2020-05-08
external identifiers
  • scopus:85089228936
ISBN
978-1-5090-6631-5
DOI
10.1109/ICASSP40776.2020.9054388
language
English
LU publication?
yes
id
866d4503-4437-47ac-9e16-b597ca78427e
date added to LUP
2020-05-07 14:57:19
date last changed
2022-04-18 22:17:21
@inproceedings{866d4503-4437-47ac-9e16-b597ca78427e,
  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.}},
  author       = {{Anderson, Rachele and Sandsten, Maria}},
  booktitle    = {{ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing}},
  isbn         = {{978-1-5090-6631-5}},
  keywords     = {{Non-parametric spectral Granger causality; multitaper spectral estimation; functional connectivity; steady-state visually evoked potentials (SSVEP); EEG}},
  language     = {{eng}},
  pages        = {{1284--1288}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Multitaper Spectral Granger Causality with Application to Ssvep}},
  url          = {{http://dx.doi.org/10.1109/ICASSP40776.2020.9054388}},
  doi          = {{10.1109/ICASSP40776.2020.9054388}},
  year         = {{2020}},
}