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A Novel Time-Frequency Parameterization Method for Oscillations in Specific Frequency Bands and Its Application on OPM-MEG.
Liang, Xiaoyu; Wang, Ruonan; Wu, Huanqi; Ma, Yuyu; Liu, Changzeng; Gao, Yang; Yu, Dexin; Ning, Xiaolin.
Afiliación
  • Liang X; School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
  • Wang R; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
  • Wu H; Hefei National Laboratory, Hefei 230088, China.
  • Ma Y; School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
  • Liu C; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
  • Gao Y; School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
  • Yu D; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
  • Ning X; School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Bioengineering (Basel) ; 11(8)2024 Jul 31.
Article en En | MEDLINE | ID: mdl-39199731
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Bioengineering (Basel) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Bioengineering (Basel) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza