title: Classification of the myoelectric signal using time-frequency based representations.
abstract: An accurate and computationally efficient means of classifying surface myoelectric signal patterns has been the subject of considerable research effort in recent years. Effective feature extraction is crucial to reliable classification and, in the quest to improve the accuracy of transient myoelectric signal pattern classification, an ensemble of time-frequency based representations are proposed. It is shown that feature sets based upon the short-time Fourier transform, the wavelet transform, and the wavelet packet transform provide an effective representation for classification, provided that they are subject to an appropriate form of dimensionality reduction.
authors.affiliation: Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada. kengleha@unb.ca
authors.forename: K
authors.initials: K
authors.lastname: Englehart
authors.forename: B
authors.initials: B
authors.lastname: Hudgins
authors.forename: P A
authors.initials: PA
authors.lastname: Parker
authors.forename: M
authors.initials: M
authors.lastname: Stevenson
mesh_terms: D000200:Action Potentials / Q000502:physiology; D004576:Electromyography / Q000145:classification* / Q000379:methods / Q000706:statistics & numerical data; D005583:Fourier Analysis; D006801:Humans; D018482:Muscle, Skeletal / Q000502:physiology*; D012016:Reference Values; D012879:Skin Physiological Phenomena; D013499:Surface Properties; D013997:Time Factors