Your browser doesn't support javascript.
loading
Multiscale Transfer Spectral Entropy for Quantifying Corticomuscular Interaction.
IEEE J Biomed Health Inform ; 25(6): 2281-2292, 2021 06.
Article em En | MEDLINE | ID: mdl-33090963
Corticomuscular coupling reflects nonlinear interactions and multi-layer neural information transmission between the motor cortex and effector muscle in the sensorimotor system. Transfer spectral entropy (TSE) method has been used to describe corticomuscular coupling within single scale. As an extension of TSE, multiscale transfer spectral entropy (MSTSE) is proposed in this paper to depict multi-layer of neural information transfer between two coupling signals. The reliability and effectiveness of MSTSE were verified on data generated by nonlinear numerical models and those of a force tracking task. Compared with TSE, MSTSE is more robust to the embedding dimension and performs optimally in the detection of the coupling properties. Further analysis of the physiological signals showed that the MSTSE provided more detailed band characteristics than the single scale TSE measurement. MSTSE indicates significant coupling scattered in alpha, beta and low gamma bands during the force tracking task. Besides, the coupling strength in the descending direction of the beta band was significantly higher than that in the ascending direction. This study constructs multi-scale coupling information to provide a new perspective for exploring corticomuscular interaction.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Músculo Esquelético / Eletroencefalografia Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: IEEE J Biomed Health Inform Ano de publicação: 2021 Tipo de documento: Article País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Músculo Esquelético / Eletroencefalografia Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: IEEE J Biomed Health Inform Ano de publicação: 2021 Tipo de documento: Article País de publicação: Estados Unidos