Corticomuscular coupling analysis based on improved LSTM and transfer entropy. (24th August 2021)

• Record Type: Journal Article
• Authors: Ye, Fei; Sun, Ziyang; Yang, Donghui; Wang, Huijiao; Xi, Xugang
• Is Part Of: Neuroscience letters. Volume 760(2021)
• Publication Date: 2021-08-24
• Subjects: Corticomuscular coupling -- Transfer entropy -- Electroencephalogram -- Electromyography; Neurology -- Periodicals; Neurology -- Periodicals; Research -- Periodicals; Neurologie -- Périodiques; Neuroanatomie -- Périodiques; Neuropharmacologie -- Périodiques; Neurophysiologie -- Périodiques; Neurology; Periodicals; Electronic journals; 617.48
• DOI: 10.1016/j.neulet.2021.136012
• Languages: English
• ISSNs: 0304-3940

Abstract

Highlights: A new method based on TE and LSTM is firstly proposed. A coupling analysis method based on coupling area is proposed. Analyze the characteristics of different human gestures from the coupling strength. Our method plays a major role in exploring the mechanism of human movement. Abstract: The study of functional corticomuscular coupling can reflect the interaction between the cerebral cortex and muscle tissue, thereby helping to understand how the brain controls muscle tissue and the effect of muscle movement on brain function. This study proposes a detection model of the coupling strength between the cortex and muscles. The detection model uses an adaptive selector to choose the optimal long short-term memory network, uses this network to extract the features of electroencephalography and electromyography, and finally transforms time characteristics into the frequency domain. The transfer entropy is used to represent the interaction intensity of signals in different frequency bands. Using this model, we analyze the coupling relationship between the cortex and muscles in the three movements of wrist flexion, wrist extension, and clench fist, and compare the model with traditional wavelet coherence analysis and deep canonical correlation analysis. The experimental results show that our model can not only express the bidirectional coupling relationship between different frequency bands but also suppress the possible false coupling that traditional methods may detect. Our research shows that the proposed model has great potential in medical rehabilitation, movement decoding, and other fields .