title: A comparison of surface and intramuscular myoelectric signal classification.
abstract: The surface myoelectric signal (MES) has been used as an input to controllers for powered prostheses for many years. As a result of recent technological advances it is reasonable to assume that there will soon be implantable myoelectric sensors which will enable the internal MES to be used as input to these controllers. An internal MES measurement should have less muscular crosstalk allowing for more independent control sites. However, it remains unclear if this benefit outweighs the loss of the more global information contained in the surface MES. This paper compares the classification accuracy of six pattern recognition-based myoelectric controllers which use multi-channel surface MES as inputs to the same controllers which use multi-channel intramuscular MES as inputs. An experiment was designed during which surface and intramuscular MES were collected simultaneously for 10 different classes of isometric contraction. There was no significant difference in classification accuracy as a result of using the intramuscular MES measurement technique when compared to the surface MES measurement technique. Impressive classification accuracy (97%) could be achieved by optimally selecting only three channels of surface MES.
authors.affiliation: Institute of Biomedical Engineering, Department of Electrical and Computer Engineering, University of New Brunswick, P.O. Box 4400, Fredericton, NB E3B 5A3, Canada. levi.hargrove@unb.ca
authors.forename: Levi J
authors.initials: LJ
authors.lastname: Hargrove
authors.forename: Kevin
authors.initials: K
authors.lastname: Englehart
authors.forename: Bernard
authors.initials: B
authors.lastname: Hudgins
mesh_terms: D000328:Adult; D017097:Electric Impedance; D004566:Electrodes; D004576:Electromyography / Q000379:methods*; D005542:Forearm / Q000033:anatomy & histology / Q000502:physiology; D006225:Hand / Q000502:physiology; D006801:Humans; D007537:Isometric Contraction / Q000502:physiology*; D008297:Male; D008954:Models, Biological; D018482:Muscle, Skeletal / Q000502:physiology*; D046709:Pattern Recognition, Physiological; D012815:Signal Processing, Computer-Assisted; D012984:Software; D014953:Wrist / Q000502:physiology