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Classification of raw myoelectric signals using finite impulse response neural networks

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3 Author(s)
Atsma, W.J. ; Inst. of Biomed. Eng., New Brunswick Univ., Fredericton, NB, Canada ; Hudgins, B. ; Lovely, D.F.

A method for classifying movement patterns of the upper arm, intended for multifunction control of arm prostheses, is presented. A finite impulse response neural network (FIRNN) is trained on 100 msec segments of myoelectric signals (MES) recorded during the very initial stage of elbow flexion (FL) and extension (EX). The network develops a clear internal representation of the input signals and is capable of classifying them

Published in:

Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE  (Volume:4 )

Date of Conference:

31 Oct-3 Nov 1996