Hilbert spectral analysis of EEG data reveals spectral dynamics associated with microstates.
Empirical mode decomposition
Hilbert spectral analysis
Microstates
Resting-state EEG
Topographic segmentation
Journal
Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558
Informations de publication
Date de publication:
01 09 2019
01 09 2019
Historique:
received:
20
09
2018
revised:
12
06
2019
accepted:
14
06
2019
pubmed:
16
7
2019
medline:
8
10
2020
entrez:
15
7
2019
Statut:
ppublish
Résumé
This study addresses an ongoing debate, i.e. whether microstates have a relation to specific oscillations or frequency bands. The previous literature on this has been inconclusive. Due to stochastic calculation of microstates it is important to address this issue because instead of providing further insights, it might lead us to ambiguous interpretations. Here we propose a new method that allows to remove the time-frequency trade-off, which hampered previous works, using Empirical Mode Decomposition (EMD) and the AM-FM model. The method is applied to two resting-state EEG datasets. First, our analysis confirmed that, indeed, when overlooking time-dependence in frequency domain, the results are inconclusive and consequently, highlighted the importance of preserving time-information in the spectral domain. Second, it is confirmed using synthetic data that the local peaks in global field potential (GFP) waveform are influenced by spectral powers present in composite signals. Based on synthetic results, it is inferred that in our dataset, an average frequency range of 10-15 Hz dominates the formation and the temporal dynamics of microstates. Third, it is shown that multiple overlapping patterns of synchronized activities described by a single meta-process in full band microstate studies can be identified using the proposed frequency-band subdivision. The results are consistent across both datasets. This study opens several new ventures to be explored in the future: e.g. analysis of temporally overlapping patterns described so far by single topographic patterns, which we show to be spectrally differentiable via band-wise topographic segmentation proposed in the present study.
Sections du résumé
BACKGROUND
This study addresses an ongoing debate, i.e. whether microstates have a relation to specific oscillations or frequency bands. The previous literature on this has been inconclusive. Due to stochastic calculation of microstates it is important to address this issue because instead of providing further insights, it might lead us to ambiguous interpretations.
NEW METHOD
Here we propose a new method that allows to remove the time-frequency trade-off, which hampered previous works, using Empirical Mode Decomposition (EMD) and the AM-FM model. The method is applied to two resting-state EEG datasets.
RESULTS
First, our analysis confirmed that, indeed, when overlooking time-dependence in frequency domain, the results are inconclusive and consequently, highlighted the importance of preserving time-information in the spectral domain. Second, it is confirmed using synthetic data that the local peaks in global field potential (GFP) waveform are influenced by spectral powers present in composite signals. Based on synthetic results, it is inferred that in our dataset, an average frequency range of 10-15 Hz dominates the formation and the temporal dynamics of microstates. Third, it is shown that multiple overlapping patterns of synchronized activities described by a single meta-process in full band microstate studies can be identified using the proposed frequency-band subdivision. The results are consistent across both datasets.
CONCLUSION
This study opens several new ventures to be explored in the future: e.g. analysis of temporally overlapping patterns described so far by single topographic patterns, which we show to be spectrally differentiable via band-wise topographic segmentation proposed in the present study.
Identifiants
pubmed: 31302155
pii: S0165-0270(19)30175-X
doi: 10.1016/j.jneumeth.2019.108317
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
108317Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.