Quantification of Feature Space Changes With Experience During Electromyogram Pattern Recognition Control

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 20; no. 3; pp. 239 - 246
• Main Authors: Bunderson, Nathan E., Kuiken, Todd A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analysis of Variance; Classification algorithms; Discriminant Analysis; Electrodes; Electromyography; Electromyography (EMG); Electromyography - methods; Female; Humans; Learning; Male; motor learning; Muscle Contraction - physiology; myoelectric control; neural machine interface; Pattern recognition; Pattern Recognition, Physiological - physiology; prosthesis; Prosthesis Design; Reproducibility of Results; Silicon; Studies; Testing; Training; User-Computer Interface; Young Adult

Pattern recognition of the electromyogram (EMG) has been demonstrated in the laboratory to be a successful alternative to conventional control methods for myoelectric prostheses. Pattern recognition control is dependent upon both machine and user learning; the user learns to generate distinct classe...