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

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...

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Published inBioengineering (Basel) Vol. 11; no. 8; p. 773
Main Authors Liang, Xiaoyu, Wang, Ruonan, Wu, Huanqi, Ma, Yuyu, Liu, Changzeng, Gao, Yang, Yu, Dexin, Ning, Xiaolin
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 01.08.2024
MDPI
Subjects
Algorithms
aperiodic component fitting
Attention
cortical oscillations
Experiments
Fourier transforms
Frequencies
Frequency spectrum
Human subjects
Magnetoencephalography
MEG
Methods
Neurosciences
Noise
Oscillations
Parameterization
Performance evaluation
Simulation
Time-frequency analysis
time–frequency parameterization
Wavelet transforms
China
cortical oscillations
aperiodic component fitting
time–frequency parameterization
MEG
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Summary: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.
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ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering11080773