Literature DB >> 10380953

Non-linear EEG dynamic changes and their probable relation to voluntary movement organization.

D Popivanov1, J Dushanova.   

Abstract

This study was undertaken to analyze systematically the non-linear dynamic changes of EEG activity accompanying slow goal-directed voluntary movements, using three non-linear characteristics (NC): point-wise correlation dimension, Kolmogorov entropy and largest Lyapunov exponents as functions of time. NC indicated transitions with non-linear properties (NT). A significant difference between times of appearance of the NT with respect to the electrode position was established: before the movement onset, NT appeared first in contralateral and midline areas including frontal, sensorimotor and parietal cortices. Before target reaching, NT appeared first in the contralateral sensorimotor area, and evolved ipsilaterally. The results suggest that the NT could be regarded as precursors of higher functional coupling between cortical areas involved in voluntary movement organization.

Entities:  

Mesh:

Year:  1999        PMID: 10380953     DOI: 10.1097/00001756-199905140-00003

Source DB:  PubMed          Journal:  Neuroreport        ISSN: 0959-4965            Impact factor:   1.837


  5 in total

1.  Detection of movement-related potentials from the electro-encephalogram for possible use in a brain-computer interface.

Authors:  E Yom-Tov; G F Inbar
Journal:  Med Biol Eng Comput       Date:  2003-01       Impact factor: 2.602

2.  Mutual-information-based approach for neural connectivity during self-paced finger lifting task.

Authors:  Chun-Chuan Chen; Jen-Chuen Hsieh; Yu-Zu Wu; Po-Lei Lee; Shyan-Shiou Chen; David M Niddam; Tzu-Chen Yeh; Yu-Te Wu
Journal:  Hum Brain Mapp       Date:  2008-03       Impact factor: 5.038

3.  Linear and nonlinear information flow based on time-delayed mutual information method and its application to corticomuscular interaction.

Authors:  Seung-Hyun Jin; Peter Lin; Mark Hallett
Journal:  Clin Neurophysiol       Date:  2009-12-30       Impact factor: 3.708

4.  Surface Electromyography and Electroencephalogram-Based Gait Phase Recognition and Correlations Between Cortical and Locomotor Muscle in the Seven Gait Phases.

Authors:  Pengna Wei; Jinhua Zhang; Baozeng Wang; Jun Hong
Journal:  Front Neurosci       Date:  2021-05-21       Impact factor: 4.677

5.  Electroencephalogram-Electromyogram Functional Coupling and Delay Time Change Based on Motor Task Performance.

Authors:  Nyi Nyi Tun; Fumiya Sanuki; Keiji Iramina
Journal:  Sensors (Basel)       Date:  2021-06-26       Impact factor: 3.576

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.