Multitaper Spectral Granger Causality with Application to Ssvep

Rachele Anderson; Maria Sandsten

doi:10.1109/ICASSP40776.2020.9054388

Multitaper Spectral Granger Causality with Application to Ssvep

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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.

Original language	English
Title of host publication	ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	1284-1288
ISBN (Electronic)	978-1-5090-6631-5
DOIs	https://doi.org/10.1109/ICASSP40776.2020.9054388
Publication status	Published - 2020 May
Event	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020 - Centre de Convencions Internacional de Barcelona (CCIB), Barcelona, Spain Duration: 2020 May 4 → 2020 May 8 https://2020.ieeeicassp.org/

Conference

Conference	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020
Abbreviated title	ICASSP 2020
Country/Territory	Spain
City	Barcelona
Period	2020/05/04 → 2020/05/08
Internet address	https://2020.ieeeicassp.org/

Subject classification (UKÄ)

Signal Processing

Free keywords

Non-parametric spectral Granger causality
multitaper spectral estimation
functional connectivity
steady-state visually evoked potentials (SSVEP)
EEG

Access to Document

10.1109/ICASSP40776.2020.9054388

1 Doctoral Thesis (compilation)

Statistical inference and time-frequency estimation for non-stationary signal classification
Anderson, R., 2019 Sept 9, Lund, Sweden: Media-Tryck, Lund University, Sweden. 172 p.
Research output: Thesis › Doctoral Thesis (compilation)

Cite this

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title = "Multitaper Spectral Granger Causality with Application to Ssvep",

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.",

keywords = "Non-parametric spectral Granger causality, multitaper spectral estimation, functional connectivity, steady-state visually evoked potentials (SSVEP), EEG",

author = "Rachele Anderson and Maria Sandsten",

year = "2020",

month = may,

doi = "10.1109/ICASSP40776.2020.9054388",

language = "English",

pages = "1284--1288",

booktitle = "ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",

url = "https://2020.ieeeicassp.org/",

}

Anderson, R & Sandsten, M 2020, Multitaper Spectral Granger Causality with Application to Ssvep. in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 1284-1288, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020, Barcelona, Spain, 2020/05/04. https://doi.org/10.1109/ICASSP40776.2020.9054388

Multitaper Spectral Granger Causality with Application to Ssvep. / Anderson, Rachele; Sandsten, Maria.
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE - Institute of Electrical and Electronics Engineers Inc., 2020. p. 1284-1288.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Multitaper Spectral Granger Causality with Application to Ssvep

AU - Anderson, Rachele

AU - Sandsten, Maria

PY - 2020/5

Y1 - 2020/5

N2 - 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.

AB - 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.

KW - Non-parametric spectral Granger causality

KW - multitaper spectral estimation

KW - functional connectivity

KW - steady-state visually evoked potentials (SSVEP)

KW - EEG

U2 - 10.1109/ICASSP40776.2020.9054388

DO - 10.1109/ICASSP40776.2020.9054388

M3 - Paper in conference proceeding

SP - 1284

EP - 1288

BT - ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020

Y2 - 4 May 2020 through 8 May 2020

ER -

Multitaper Spectral Granger Causality with Application to Ssvep

Abstract

Conference

Subject classification (UKÄ)

Free keywords

Access to Document

Fingerprint

Research output

Statistical inference and time-frequency estimation for non-stationary signal classification

Cite this