Abstract

Info

Title:: Construction of motion imagination causal network based on multivariable Granger causality

Author(s):: CHANG Wenqing; SUN Yao; School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

Keywords:: ; Keywords: multivariable Granger causality; motion imagination; causal network

Abstract:: Abstract: The exploration of the rules of neural activity in motion imagination can provide theoretical guidance for the development of new methods of rehabilitation training for patients with cerebral injuries and acroparalysis. The causal network constructed based on Granger causality is an important tool for analyzing the state of neural activity in motion imagination. However, Granger causality can only reflect the interaction between two variables, and a simple motion imagination process requires multiple neural nodes to participate. To solve this problem, a multivariable Granger causal analysis that can reflect the interaction among multiple variables in a cluster is introduced to optimize the construction method of motion imagination causal network. For the 4 subjects, multivariate and traditional Granger causality relationships are used to construct two different motion imaging patterns of the same subject, and the network characteristics are extracted to classify the motion imaginary patterns. The results show that the accuracy rate of motion imaging pattern classification in 4 subjects is 90.4%, 88.8%, 91.1%, and 90.3% in multivariable Granger causal network, as compared with 88.5%, 89.3%, 90.2%, 89.7% in traditional Granger causal network. Compared with the traditional Granger causality, causal network based on multivariable Granger causality can more accurately reflect the characteristics of neural activity in motion imagination.