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Xiaoling Chen

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YNICL Journal 2018 Journal Article

Abnormal functional corticomuscular coupling after stroke

  • Xiaoling Chen
  • Ping Xie
  • Yuanyuan Zhang
  • Yuling Chen
  • Shengcui Cheng
  • Litai Zhang

Motor dysfunction is a major consequence after stroke and it is generally believed that the loss of motor ability is caused by the impairments in neural network that controls movement. To explore the abnormal mechanisms how the brain controls shoulder abduction and elbow flexion in "flexion synergy" following stroke, we used the functional corticomuscular coupling (FCMC) between the brain and the muscles as a tool to identify the temporal evolution of corticomuscular interaction between the synkinetic and separate phases. 59-channel electroencephalogram (EEG) over brain scalp and 2-channel electromyogram (EMG) from biceps brachii (BB)/deltoid (DT) were recorded in sixteen stroke patients with motor dysfunction and eight healthy controls during a task of uplifting the arm (stage 1) and maintaining up to the chest (stage 2). As a result, compared to healthy controls, stroke patients had abnormally reduced coherence in EEG-BB combination and increased coherence in EEG-DT combination. Compared to synkinetic stroke patients, separate ones exhibited higher coupling at gamma-band during stage 1 and higher at beta-band during stage 2 in EEG-BB combination, but lower at beta-band during stage 2 in EEG-DT combination. Therefore, we infer that the disorders of efferent control and afferent proprioception in sensorimotor system for stroke patients effect on the oscillation at beta and gamma bands. Patients need integrate more information for shoulder abduction to compensate for the functional loss of elbow flexion in the recovery process, so that partial cortical cortex controlling on the elbow flexion may work on the shoulder abduction during "flexion synergy". Such researches could provide new perspective on the temporal evolution of corticomuscular interaction after stroke and add to our understanding of possible pathomechanisms how the brain abnormally controls shoulder abduction and elbow flexion in "flexion synergy".

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ICRA Conference 2014 Conference Paper

A synchronous and multi-domain feature extraction method of EEG and sEMG in power-assist rehabilitation robot

  • Yan Song
  • Yihao Du
  • Xiaoguang Wu
  • Xiaoling Chen
  • Ping Xie

To propose a synchronous and multi-domain feature extraction method of electroencephalogram (EEG) and surface electromyogram (sEMG) signals is of great significance to power-assist rehabilitation robot control with humancomputer interface (HCI). In this paper, nonnegative Tucker decomposition which is one model of nonnegative tensor factorization (NTF) is used to fuse two kinds of bioelectricity signals (EEG and sEMG) and extract multi-domain features of EEG and sEMG signals for classification which contain time, frequency, and space domains. In the first step the EEG and sEMG data are transformed into multidimensional information using continuous wavelet transform and the 4-D EEG-sEMG tensor is established. Then the tensor is decomposed into four components (spatial components, spectral components, temporal components and category components) and the core tensor is the feature extracted. The feature after being eliminated and compressed are fed into KNN, LDA and SVM classifiers for pattern recognition, and a comparison is done in single EEG analysis, single sEMG analysis and both EEG and sEMG analysis. An experiment about 10 healthy participants' upper limb movements was carried out to verify the validity of this algorithm. The result implied that NTF is a meaningful and valuable synchronous and multi-domain feature extraction method which may be promising in power-assist rehabilitation robot control.

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