Nonlinear Dynamics, Psychology, and Life Sciences, Vol. 18, Iss. 3, July, 2014, pp. 229-249
@2014 Society for Chaos Theory in Psychology & Life Sciences


Can Surface EMG Be Adequately Described by Digital Sampling?

Lei Min, Shanghai Jiao Tong University, China; Vanderbilt University, Nashville, TN
Nilanjan Sarkar, Vanderbilt University, Nashville, TN
Meng Guang, Shanghai Jiao Tong University, China
Gu Yudong, Hua Shan Hospital, Fudan University, China
Zhang Kaili, Hua Shan Hospital, Fudan University, China
Tian Dong, Hua Shan Hospital, Fudan University, China

Abstract: Surface electromyography (SEMG) is a common tool to evaluate muscle function in kinesiological studies, musculoskeletal rehabilitation, prosthetics, clinical research and neurological disease diagnosis. The acquisition of SEMG is a crucially basic issue to gain an insight into musculoskeletal system function. The aim of this study is to investigate if the sampled surface EMG signals can reflect adequately the neural activity of the underlying musculature. The surface EMG signals of four muscles (abductor pollicis muscles and abductor digiti minimi muscles of right hand and left hand) are studied on the amplitude, frequency and nonlinear measure based on symplectic geometry. There are obvious differences in nonlinear measures of the different sampled signals, although there are little significant changes in their amplitude and frequency measures. Meanwhile, surface EMG signals obviously differ from their surrogate data at higher sampling frequencies. The results indicate that surface EMG signals contain nonlinear components. To gather the sufficient information of surface EMG signal, the data acquisition should be required at the higher sampling frequency. Furthermore, the nonlinear measure based on symplectic geometry can be used as a sensitive index for evaluation of the activity of the human muscles.

Keywords: nonlinear, symplectic geometry, surface electromyography, sampling frequency