A Novel Time-Frequency Parameterization Method for Oscillations in Specific Frequency Bands and Its Application on OPM-MEG.

Xiaoyu Liang, Ruonan Wang, Huanqi Wu, Yuyu Ma, Changzeng Liu, Yang Gao, Dexin Yu, Xiaolin Ning

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

A neuroscientist's guide to neural burst detection.Imaging neuroscience (Cambridge, Mass.)
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Authors and funding

8 authors.

Xiaoyu LiangSchool of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.ORCID 0000-0002-2978-2742

Ruonan WangSchool of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.ORCID 0000-0002-0018-8213

Huanqi WuSchool of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.ORCID 0000-0001-5728-7418

Yuyu MaSchool of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

Changzeng LiuSchool of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

Yang GaoSchool of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

Dexin YuShandong Key Laboratory: Magnetic Field-Free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan 250012, China.

Xiaolin NingSchool of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

Funding

Industry Technology Infrastructure Public Service Platform Project, Ministry of Industry and In-formation Technology 2022189181Innovation Program for Quantum Science and Technology 2021ZD0300500/2021ZD0300503Key Laboratory of Weak Magnetic Detection Technology of Ministry of Education and Beijing Municipal Natural Science Foundation 4212012Key R&D Program of Shandong Province 2022ZLGX03National Natural Science Foundation of China 42388101

6 · The paper itself

Abstract

Time-frequency parameterization for oscillations in specific frequency bands reflects the dynamic changes in the brain. It is related to cognitive behavior and diseases and has received significant attention in neuroscience. However, many studies do not consider the impact of the aperiodic noise and neural activity, including their time-varying fluctuations. Some studies are limited by the low resolution of the time-frequency spectrum and parameter-solved operation. Therefore, this paper proposes super-resolution time-frequency periodic parameterization of (transient) oscillation (STPPTO). STPPTO obtains a super-resolution time-frequency spectrum with Superlet transform. Then, the time-frequency representation of oscillations is obtained by removing the aperiodic component fitted in a time-resolved way. Finally, the definition of transient events is used to parameterize oscillations. The performance of this method is validated on simulated data and its reliability is demonstrated on magnetoencephalography. We show how it can be used to explore and analyze oscillatory activity under rhythmic stimulation.

Indexed as

aperiodic component fittingcortical oscillationsMEGtime–frequency parameterization

Identifiers

PMID39199731

DOI10.3390/bioengineering11080773

PMCPMC11351447

What Socratic holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.