Effectively combining temporal projection noise suppression methods in magnetoencephalography.
Artifact suppression
HFO
Magnetoencephalography
Sensor noise suppression
Temporal projection
Journal
Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558
Informations de publication
Date de publication:
15 07 2020
15 07 2020
Historique:
received:
07
11
2019
revised:
11
03
2020
accepted:
24
03
2020
pubmed:
18
5
2020
medline:
22
6
2021
entrez:
17
5
2020
Statut:
ppublish
Résumé
Magnetoencephalography (MEG) is an excellent non-invasive tool to study the brain. However, measurements often suffer from the contribution of external interference, including noise from the sensors. Suppression of noise from the data is critical for an accurate representation of brain signals. Due to MEG's limited spatial resolution and superior temporal resolution, noise suppression methods that operate in the temporal domain can be favorable. We examined the independent and joint effects of two temporal projection noise suppression algorithms for MEG measurements: One commonly used algorithm which suppresses correlated noise; temporal signal space separation (tSSS) and one new method which suppresses uncorrelated sensor noise; oversampled temporal projection (OTP). We found that both OTP and tSSS effectively suppress noise in raw MEG data and have the greatest effect of joint operation in cases where SNR is low, or when detecting higher SNR single-trial responses from raw data. We additionally demonstrate how the combination of OTP and tSSS is useful for the detectability of high-frequency brain oscillations (HFO). Although the mathematical description of OTP has been described before (Larson and Taulu, 2017), OTP's effect on HFOs in MEG data is novel. Additionally, the combination of OTP and commonly used temporal noise suppression algorithms (i.e., tSSS) has not been shown. This finding is applicable to clinical populations such as epilepsy, where HFO signals are thought to be important markers for areas of seizure onset and are typically difficult to detect with non-invasive neuroimaging methods.
Sections du résumé
BACKGROUND
Magnetoencephalography (MEG) is an excellent non-invasive tool to study the brain. However, measurements often suffer from the contribution of external interference, including noise from the sensors. Suppression of noise from the data is critical for an accurate representation of brain signals. Due to MEG's limited spatial resolution and superior temporal resolution, noise suppression methods that operate in the temporal domain can be favorable.
NEW METHOD
We examined the independent and joint effects of two temporal projection noise suppression algorithms for MEG measurements: One commonly used algorithm which suppresses correlated noise; temporal signal space separation (tSSS) and one new method which suppresses uncorrelated sensor noise; oversampled temporal projection (OTP).
RESULTS
We found that both OTP and tSSS effectively suppress noise in raw MEG data and have the greatest effect of joint operation in cases where SNR is low, or when detecting higher SNR single-trial responses from raw data. We additionally demonstrate how the combination of OTP and tSSS is useful for the detectability of high-frequency brain oscillations (HFO).
COMPARISON WITH EXISTING METHODS
Although the mathematical description of OTP has been described before (Larson and Taulu, 2017), OTP's effect on HFOs in MEG data is novel. Additionally, the combination of OTP and commonly used temporal noise suppression algorithms (i.e., tSSS) has not been shown.
CONCLUSIONS
This finding is applicable to clinical populations such as epilepsy, where HFO signals are thought to be important markers for areas of seizure onset and are typically difficult to detect with non-invasive neuroimaging methods.
Identifiants
pubmed: 32416275
pii: S0165-0270(20)30123-0
doi: 10.1016/j.jneumeth.2020.108700
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
108700Informations de copyright
Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors report that Dr. Samu Taulu and Dr. Eric Larson are consultants for S3 Signal Processing, a company that has a commercial interest in the methodology presented in this manuscript.