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. 2018 Jan 4:8:716.
doi: 10.3389/fneur.2017.00716. eCollection 2017.

Electroencephalogram-Electromyography Coupling Analysis in Stroke Based on Symbolic Transfer Entropy

Yunyuan Gao  1 , Leilei Ren  1 , Rihui Li  2   3 , Yingchun Zhang  1   2   3
Affiliations

Electroencephalogram-Electromyography Coupling Analysis in Stroke Based on Symbolic Transfer Entropy

Yunyuan Gao et al. Front Neurol. .

Abstract

The coupling strength between electroencephalogram (EEG) and electromyography (EMG) signals during motion control reflects the interaction between the cerebral motor cortex and muscles. Therefore, neuromuscular coupling characterization is instructive in assessing motor function. In this study, to overcome the limitation of losing the characteristics of signals in conventional time series symbolization methods, a variable scale symbolic transfer entropy (VS-STE) analysis approach was proposed for corticomuscular coupling evaluation. Post-stroke patients (n = 5) and healthy volunteers (n = 7) were recruited and participated in various tasks (left and right hand gripping, elbow bending). The proposed VS-STE was employed to evaluate the corticomuscular coupling strength between the EEG signal measured from the motor cortex and EMG signal measured from the upper limb in both the time-domain and frequency-domain. Results showed a greater strength of the bi-directional (EEG-to-EMG and EMG-to-EEG) VS-STE in post-stroke patients compared to healthy controls. In addition, the strongest EEG-EMG coupling strength was observed in the beta frequency band (15-35 Hz) during the upper limb movement. The predefined coupling strength of EMG-to-EEG in the affected side of the patient was larger than that of EEG-to-EMG. In conclusion, the results suggested that the corticomuscular coupling is bi-directional, and the proposed VS-STE can be used to quantitatively characterize the non-linear synchronization characteristics and information interaction between the primary motor cortex and muscles.

Keywords: corticomuscular coupling; electroencephalogram; electromyography; stroke; symbolic transfer entropy.

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Figures

Figure 1
Figure 1
Illustration of the experimental paradigm.
Figure 2
Figure 2
(A) Experimental environment of the electroencephalogram and electromyography (EMG) data measurement; (B) illustration of the locations of EMG electrodes on upper limb.
Figure 3
Figure 3
Different delay time of TE with respect to the direction of information flow.
Figure 4
Figure 4
Mean and SD of the STE hand gripping task with respect to different scale parameters. (A) Left hand 5 kg gripping; (B) right hand 5 kg gripping; (C) left hand 10 kg gripping; (D) right hand 10 kg gripping; (E) left hand elbow bend; (F) right hand elbow bend.
Figure 5
Figure 5
The bi-directional STEs between electroencephalogram and electromyography with respect to different tasks. (A) Left hand 5 kg gripping; (B) right hand 5 kg gripping; (C) left hand 10 kg gripping; (D) right hand 10 kg gripping; (E) left hand elbow flexion; (F) right hand elbow flexion. PA, patients; HC, healthy controls; STE, symbolic transfer entropy.
Figure 6
Figure 6
Summarized coupling strength (CS) of patient group (S1–S5) with respect to various motor tasks. (A) Left 5 kg; (B) right 5 kg; (C) left 10 kg; (D) right 10 kg; (E) left elbow flexion; (F) right elbow flexion.
Figure 7
Figure 7
Mean coupling strength (CS) of healthy subjects (S6–S12) with respect to various motor tasks. (A) Left 5 kg; (B) right 5 kg; (C) left 10 kg; (D) right 10 kg; (E) left elbow flexion; (F) right elbow flexion.
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