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EEG dynamical network analysis method reveals the neural signature of visual-motor coordination

Li, X., Mota, B., Kondo, T., Nasuto, S. and Hayashi, Y. (2020) EEG dynamical network analysis method reveals the neural signature of visual-motor coordination. PLoS ONE, 15 (5). e0231767. ISSN 1932-6203

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To link to this item DOI: 10.1371/journal.pone.0231767


Human visual-motor coordination is an essential function of movement control, which requires interactions of multiple brain regions. Understanding the cortical-motor coordination is important for improving physical therapy for motor disabilities. However, its underlying transient neural dynamics is still largely unknown. In this study, we applied an eigenvector-based dynamical network analysis method to investigate the functional connectivity calculated from electroencephalography (EEG) signals under visual-motor coordination task and to identify its meta-stable states dynamics. We first tested this signal processing on a simulated network to evaluate it in comparison with other dynamical methods, demonstrating that the eigenvector-based dynamical network analysis was able to correctly extract the dynamical features of the evolving networks. Subsequently, the eigenvector-based analysis was applied to EEG data collected under a visual-motor coordination experiment. In the EEG study with participants, the results of both topological analysis and the eigenvector-based dynamical analysis were able to distinguish different experimental conditions of visual tracking task. With the dynamical analysis, we showed that different visual-motor coordination states can be distinguished by investigating the meta-stable states dynamics of the functional connectivity.

Item Type:Article
Divisions:Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:90914
Publisher:Public Library of Science


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