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Applying support vector regression analysis on grip force level-related corticomuscular coherence

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Abstract

Voluntary motor performance is the result of cortical commands driving muscle actions. Corticomuscular coherence can be used to examine the functional coupling or communication between human brain and muscles. To investigate the effects of grip force level on corticomuscular coherence in an accessory muscle, this study proposed an expanded support vector regression (ESVR) algorithm to quantify the coherence between electroencephalogram (EEG) from sensorimotor cortex and surface electromyogram (EMG) from brachioradialis in upper limb. A measure called coherence proportion was introduced to compare the corticomuscular coherence in the alpha (7–15Hz), beta (15–30Hz) and gamma (30–45Hz) band at 25 % maximum grip force (MGF) and 75 % MGF. Results show that ESVR could reduce the influence of deflected signals and summarize the overall behavior of multiple coherence curves. Coherence proportion is more sensitive to grip force level than coherence area. The significantly higher corticomuscular coherence occurred in the alpha (p < 0.01) and beta band (p < 0.01) during 75 % MGF, but in the gamma band (p < 0.01) during 25 % MGF. The results suggest that sensorimotor cortex might control the activity of an accessory muscle for hand grip with increased grip intensity by changing functional corticomuscular coupling at certain frequency bands (alpha, beta and gamma bands).

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Acknowledgments

This research was partially sponsored by Natural Science Foundation of China (No. 81071231, 61175115 and 61370113), Beijing Natural Science Foundation (7132028&7132021), the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (CIT&TCD201304035), Jing-Hua Talents Project of Beijing University of Technology (2014-JH-L06).

Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. College of Life Science and Bioengineering, Beijing University of Technology, Beijing, People’s Republic of China, 100124

    Yao Rong, Dongmei Hao, Liu Cao, Mingai Li, Lijuan Duan & Yanjun Zeng

  2. Medical Engineering Division, Xuanwu Hospital, Capital Medical University, Beijing, China, 100053

    Yao Rong

  3. Department of Informatics and Mathematical Modeling, Technical University of Denmark, Kgs. Lyngby, Denmark, 2800

    Xixuan Han

  4. Institute of Medical Information, School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 510515

    Qing Wang

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  1. Yao Rong
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  2. Xixuan Han
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  3. Dongmei Hao
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  4. Liu Cao
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  5. Qing Wang
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  6. Mingai Li
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  7. Lijuan Duan
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  8. Yanjun Zeng
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Correspondence to Dongmei Hao or Yanjun Zeng.

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Rong, Y., Han, X., Hao, D. et al. Applying support vector regression analysis on grip force level-related corticomuscular coherence. J Comput Neurosci 37, 281–291 (2014). https://doi.org/10.1007/s10827-014-0501-0

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  • DOI: https://doi.org/10.1007/s10827-014-0501-0

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