Multitaper Spectral Granger Causality with Application to Ssvep
(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:
https://lup.lub.lu.se/record/866d4503-4437-47ac-9e16-b597ca78427e
- author
- Anderson, Rachele LU and Sandsten, Maria LU
- organization
- publishing date
- 2020-05
- 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}}, }