title: Surface electromyogram during voluntary contraction: processing tools and relation to physiological events.
abstract: Surface electromyography (SEMG) has been used extensively in the last years in a variety of applications, including muscle function assessment, pathology identification, ergonomics, pattern analysis, or population characterization. Advanced processing methods, especially in the spectral domain, provide the research worker with more and more precise and user-friendly tools for signal characterization, analysis, and classification. The use of such sophisticated tools requires many assumptions on signal characteristics, and the wide variety of computing options related to each processing method makes it difficult to compare the results of different works when these options are omitted in the reports or improperly applied. This work first aims at taking stock of the various processing methods which have emerged in the last years around surface electromyography: signal acquisition, random feature extraction, time and spectral parameter determination, statistical tests application. The main methods are briefly explained and discussed, then variations between apparently equivalent methods are pointed out, necessary hypotheses are underlined, and the use of such methods in SEMG processing is shown with respect to the more recent works. A second section shows how authors deal with parameters extracted from SEMG in order to relate them to physiological modifications (force, fiber type, fiber environment).
authors.affiliation: URA CNRS 858, Département de Génie Biologique, Université de Technologie de Compiègne, France.
authors.forename: J
authors.initials: J
authors.lastname: Duchêne
authors.forename: F
authors.initials: F
authors.lastname: Goubel
mesh_terms: D000328:Adult; D000368:Aged; D004566:Electrodes; D004576:Electromyography / Q000295:instrumentation; D006801:Humans; D007537:Isometric Contraction / Q000502:physiology; D009119:Muscle Contraction / Q000502:physiology*; D009132:Muscles / Q000378:metabolism; D012815:Signal Processing, Computer-Assisted