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Applying support vector regression analysis on grip force level-related corticomuscular coherence |
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| Authors: | Yao Rong Xixuan Han Dongmei Hao Liu Cao Qing Wang Mingai Li Lijuan Duan Yanjun Zeng |
| Institution: | 1. College of Life Science and Bioengineering, Beijing University of Technology, Beijing, People’s Republic of China, 100124 2. Medical Engineering Division, Xuanwu Hospital, Capital Medical University, Beijing, China, 100053 3. Department of Informatics and Mathematical Modeling, Technical University of Denmark, Kgs. Lyngby, Denmark, 2800 4. Institute of Medical Information, School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 510515 |
| 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?p?p? |
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