Over the past three decades we have continuously expressed concern regarding the insufficient attention given by manufacturers and users to the manner in which the surface EMG signal is detected, and more importantly, how it is analyzed and interpreted to draw inferences about the behavior of the muscles. All sensor technology introduces noise components to the EMG signal. All recordings of the EMG signal are contaminated with noise. It is important that the user understand these contaminations before they draw conclusions about the activity of the muscle from which the signal is detected. This is a critically important issue. To assist the user in appreciating these pervasive problems, Delsys has prepared a webinar that highlights these concerns and provides suggestions based on empirical research findings on how to mitigate noise contaminations.
We have strived over the past two decades to provide the EMG user with the best possible sensor technology. Our aims have been to simplify the use of the sensors, to provide stability in the recorded signal, and to minimize the influence of contaminant factors, such as ambient noise, motion artifact and cross-talk from adjacent muscles. Our designs are grounded in realistic, factual, empirical measurements that have been patented and published in the peer-reviewed literature, wherein we explain the reasoning for our designs for all to appreciate. See references in Publication list below.
We recommend a fixed 1 cm inter-electrode spacing. This spacing ensures signal stability and reduces the contaminating cross-talk signal from adjacent muscles. It also allows recording of EMG signals from small muscles, such as those of the hand, forearm, face, and neck, among other locations. We recommend a low-frequency cut-off of 20 Hz for the EMG signal. This cut-off is based on the empirical observation that the frequency range between 5 and 20 Hz contains minimal (about 4%) EMG signal energy, while most of the energy derives from movement artifact.
Zaheer F, Roy SH, De Luca CJ. Preferred sensor sites for surface EMG signal detection. Physiological Measurement, 33(2):195-206, 2012. PMID: 22260842.
De Luca CJ, Kuznetsov M, Gilmore LD and Roy SH. Inter-electrode spacing of surface EMG sensors: Reduction of crosstalk contamination during voluntary contractions. Journal of Biomechanics,45(3): 555-561, 2011. PMID: 22169134.
De Luca CJ, Gilmore LD, Kuznetsov M, and Roy SH. Filtering the Surface EMG signal: Movement artifact and baseline noise contamination. Journal of Biomechanics, 43 (8): 1573- 1579, 2010. PMID: 20206934.
Roy SH, De Luca G, Cheng S, Johansson A, Gilmore LD, and De Luca CJ. Electro-Mechanical stability of surface EMG sensors. Medical & Biological Engineering & Computing, 45: 447-457, 2007. PMID: 17458582.
De Luca CJ and Merletti R. Surface myoelectric crosstalk among muscles of the leg. Electroencephalography and Clinical Neurophysiology, 69: 568-575, 1988. PMID: 2453334.
Roy SH, De Luca CJ, and Schneider J. Effects of electrode location on myoelectric conduction velocity and median frequency estimates. Journal of Applied Physiology, 61: 1510-1517, 1986. PMID: 3781964.
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