Coupled CP Decomposition of Simultaneous MEG-EEG Signals for Differentiating Oscillators During Photic Driving.
alpha band
electroencephalography
frequency entrainment
magnetoencephalography
simultaneous diagonalization
steady-state evoked response
tensor
theta band
Journal
Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481
Informations de publication
Date de publication:
2020
2020
Historique:
received:
17
06
2019
accepted:
09
03
2020
entrez:
25
4
2020
pubmed:
25
4
2020
medline:
25
4
2020
Statut:
epublish
Résumé
Magnetoencephalography (MEG) and electroencephalography (EEG) are contemporary methods to investigate the function and organization of the brain. Simultaneously acquired MEG-EEG data are inherently multi-dimensional and exhibit coupling. This study uses a coupled tensor decomposition to extract the signal sources from MEG-EEG during intermittent photic stimulation (IPS). We employ the Coupled Semi-Algebraic framework for approximate CP decomposition via SImultaneous matrix diagonalization (C-SECSI). After comparing its performance with alternative methods using simulated benchmark data, we apply it to MEG-EEG recordings of 12 participants during IPS with fractions of the individual alpha frequency between 0.4 and 1.3. In the benchmark tests, C-SECSI is more accurate than SECSI and alternative methods, especially in ill-conditioned scenarios, e.g., involving collinear factors or noise sources with different variances. The component field-maps allow us to separate physiologically meaningful oscillations of visually evoked brain activity from background signals. The frequency signatures of the components identify either an entrainment to the respective stimulation frequency or its first harmonic, or an oscillation in the individual alpha band or theta band. In the group analysis of both, MEG and EEG data, we observe a reciprocal relationship between alpha and theta band oscillations. The coupled tensor decomposition using C-SECSI is a robust, powerful method for the extraction of physiologically meaningful sources from multidimensional biomedical data. Unsupervised signal source extraction is an essential solution for rendering advanced multi-modal signal acquisition technology accessible to clinical diagnostics, pre-surgical planning, and brain computer interface applications.
Identifiants
pubmed: 32327966
doi: 10.3389/fnins.2020.00261
pmc: PMC7161426
doi:
Types de publication
Journal Article
Langues
eng
Pagination
261Informations de copyright
Copyright © 2020 Naskovska, Lau, Korobkov, Haueisen and Haardt.
Références
Brain Res Brain Res Rev. 1999 Apr;29(2-3):169-95
pubmed: 10209231
Front Hum Neurosci. 2016 Aug 18;10:413
pubmed: 27588002
Braz J Med Biol Res. 2001 Dec;34(12):1573-84
pubmed: 11717711
Front Hum Neurosci. 2016 Feb 03;10:10
pubmed: 26869898
Physiol Meas. 2016 Jul;37(7):1146-62
pubmed: 27328313
IEEE Trans Biomed Eng. 2002 Nov;49(11):1279-86
pubmed: 12450358
Neuroreport. 2006 Nov 27;17(17):1829-33
pubmed: 17164673
Conf Proc IEEE Eng Med Biol Soc. 2015;2015:6983-6
pubmed: 26737899
Brain Sci. 2018 Mar 30;8(4):
pubmed: 29601538
J Clin Neurophysiol. 2012 Feb;29(1):33-41
pubmed: 22353983
Exp Brain Res. 2001 Apr;137(3-4):346-53
pubmed: 11355381
IEEE Trans Biomed Eng. 2011 Nov;58(11):3069-77
pubmed: 21712153
Proc SIAM Int Conf Data Min. 2014;2014:118-126
pubmed: 26473087
Electroencephalogr Clin Neurophysiol. 1991 Aug;79(2):81-93
pubmed: 1713832