Abstract：Balance is the foundation of all human movements, and existing methods of assessing human balance are mostly based on external performance. In this paper, we took the endogenous perspective of balance central neuromodulation as an entry point to study the sensorimotor cortical integration in the process of static balance regulation, analyze the activation state of the cerebral cortex, and establish an entropic network of static balance EEG transmission. The experimental paradigm was designed under the conditions of differentiation of visual and proprioceptive inputs, and the phase synchronization criterion of balance EEG was defined. The phase synchronization relationship of the transmission between the brain regions of balance information was defined. Based on the EEG data of 20 subjects, the central nervous system regulation period of balance events was determined based on the phase synchronization relationship, various endogenous characteristics of balanced EEG were extracted, and the average classification accuracy was improved by 14.66% compared with the traditional network feature classification results by using a combination of network clustering coefficient (C), shortest path (E) and maximum Lyapunov index (MLE) [C, E, MLE]. The new feature of maximum Lyapunov index (M) added in the analysis of transfer entropy network fully expressed the internal law evolution process of human balance adjustment and improved the classification ability of human balance.
苏巧钻, 罗志增, 王哲远. 视觉和本体觉差异化条件下的平衡脑电传递熵网络分析方法[J]. 中国生物医学工程学报, 2024, 43(3): 286-294.
Su Qiaozuan, Luo Zhizeng, Wang Zheyuan. Analysis Method of Brain Region Entropy of Human Balance Function Based on Differentiatedfrom Visual Proprioception. Chinese Journal of Biomedical Engineering, 2024, 43(3): 286-294.
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