Filtering the surface EMG signal: Movement artifact and baseline noise contamination

• Published in: Journal of biomechanics Vol. 43; no. 8; pp. 1573 - 1579
• Main Authors: De Luca, Carlo J, Donald Gilmore, L, Kuznetsov, Mikhail, Roy, Serge H
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 28.05.2010; Elsevier; Elsevier Limited
• Subjects: Accelerometers; Adult; Algorithms; Baseline noise; Biological and medical sciences; Biomechanics; Contamination; Diagnosis, Computer-Assisted - methods; Electrodes; Electrodiagnosis. Electric activity recording; Electromyography; Electromyography - methods; Electronics; EMG signal; Female; Filtering; Humans; Investigative techniques, diagnostic techniques (general aspects); Kinesiology; Male; Medical sciences; Middle Aged; Movement; Movement - physiology; Movement artifact; Muscle Contraction - physiology; Muscle, Skeletal - physiology; Muscles; Nervous system; Noise; Physical Medicine and Rehabilitation; Reproducibility of Results; Sensitivity and Specificity; Sensors; Signal Processing, Computer-Assisted; Skin; Spectrum allocation; Young Adult; EMG signal; Baseline noise; Filtering; Movement artifact; Human; Motion; Electrophysiology; Artefact; Biomechanics; Electrodiagnosis; Accelerometer; Signal processing; Electromyography; Biomedical engineering
• ISSN: 0021-9290; 1873-2380
• DOI: 10.1016/j.jbiomech.2010.01.027

Abstract The surface electromyographic (sEMG) signal that originates in the muscle is inevitably contaminated by various noise signals or artifacts that originate at the skin-electrode interface, in the electronics that amplifies the signals, and in external sources. Modern technology is substantially immune to some of these noises, but not to the baseline noise and the movement artifact noise. These noise sources have frequency spectra that contaminate the low-frequency part of the sEMG frequency spectrum. There are many factors which must be taken into consideration when determining the appropriate filter specifications to remove these artifacts; they include the muscle tested and type of contraction, the sensor configuration, and specific noise source. The band-pass determination is always a compromise between (a) reducing noise and artifact contamination, and (b) preserving the desired information from the sEMG signal. This study was designed to investigate the effects of mechanical perturbations and noise that are typically encountered during sEMG recordings in clinical and related applications. The analysis established the relationship between the attenuation rates of the movement artifact and the sEMG signal as a function of the filter band pass. When this relationship is combined with other considerations related to the informational content of the signal, the signal distortion of filters, and the kinds of artifacts evaluated in this study, a Butterworth filter with a corner frequency of 20 Hz and a slope of 12 dB/oct is recommended for general use. The results of this study are relevant to biomechanical and clinical applications where the measurements of body dynamics and kinematics may include artifact sources.