O. Bai, Dandan Huang, P. Lin, Jinglong Wu, Xuedong Chen, D. Fei
Corticomuscular coupling estimated by EEG-EMG coherence may reveal functional cortical driving of peripheral muscular activity. EEG-EMG coherence in the beta band (15–30 Hz) has been extensively studied under isometric muscle contraction tasks. We attempted to study the time-course of corticomuscular connectivity under a dynamic target tracking task. A new device was developed for the real-time measurement of dynamic force created by pinching thumb and index fingers. Four healthy subjects who participated in this study were asked to track visual targets with the feedback forces. Spectral parameters using FFT and complex wavelet were explored for reliable estimation of event-related coherence and EEG-EMG correlogram for representing corticomuscular connectivity. Clearly distinguishable FFT-based coherence and cross-correlogram during the visual target tracking were observed with appropriate hyper-parameters for spectral estimation. The system design and the exploration of signal processing methods in this study supports further exploration of corticomuscular connectivity associated with human motor control.
{"title":"An Event-Related Study for Dynamic Analysis of Corticomuscular Connectivity","authors":"O. Bai, Dandan Huang, P. Lin, Jinglong Wu, Xuedong Chen, D. Fei","doi":"10.4137/BECB.S5546","DOIUrl":"https://doi.org/10.4137/BECB.S5546","url":null,"abstract":"Corticomuscular coupling estimated by EEG-EMG coherence may reveal functional cortical driving of peripheral muscular activity. EEG-EMG coherence in the beta band (15–30 Hz) has been extensively studied under isometric muscle contraction tasks. We attempted to study the time-course of corticomuscular connectivity under a dynamic target tracking task. A new device was developed for the real-time measurement of dynamic force created by pinching thumb and index fingers. Four healthy subjects who participated in this study were asked to track visual targets with the feedback forces. Spectral parameters using FFT and complex wavelet were explored for reliable estimation of event-related coherence and EEG-EMG correlogram for representing corticomuscular connectivity. Clearly distinguishable FFT-based coherence and cross-correlogram during the visual target tracking were observed with appropriate hyper-parameters for spectral estimation. The system design and the exploration of signal processing methods in this study supports further exploration of corticomuscular connectivity associated with human motor control.","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BECB.S5546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70685997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-01-01DOI: 10.1177/117959721000200001
Chia-Hua Chuang, Chun‐Liang Lin
Gene networks in biological systems are not only nonlinear but also stochastic due to noise corruption. How to accurately estimate the internal states of the noisy gene networks is an attractive issue to researchers. However, the internal states of biological systems are mostly inaccessible by direct measurement. This paper intends to develop a robust extended Kalman filter for state and parameter estimation of a class of gene network systems with uncertain process noises. Quantitative analysis of the estimation performance is conducted and some representative examples are provided for demonstration.
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Pub Date : 2009-01-01DOI: 10.1177/117959720900100001
K. Najarian
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://www.creativecommons.org/licenses/by/2.0) which permits unrestricted use, distribution and reproduction provided the original work is properly cited. Open Access Full open access to this and thousands of other papers at http://www.la-press.com. Biomedical Engineering and Computational Biology
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