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Myoelectric signals (MES), which are generated by muscle contraction, are utilized in myoelectric control of powered upper-extremity prostheses. This is an effective and noninvasive method, for individuals with amputations or congenitally deficient upper limbs [1]. The MES has unique patterns containing the contraction information and correspondingly can be extracted in the form of feature vectors. Therefore, pattern recognition techniques have been extensively used as effective methods for myoelectric control [2][3]. The MES can be recorded by surface electrodes or needle electrodes, placed at several muscle sites [4]. Although the MES has been used for myoelectric control for many years, inherent characteristics limit their efficacy in wider use. Surface electrodes have limited information due to the fact that they cannot measure the contribution from deep muscles; moreover, the pick-up areas of surface electrodes are rather large, thus identifying information from certain target muscles is difficult due to crosstalk form adjacent muscles. Embedded electrodes, such as needle electrodes, solve those problems to a certain extent. Their recording areas are much smaller, thus, due to the small structure, they can be inserted into any target muscle to provide localized signals. Their use in myoelectric control however is confounded by the sensitivity to electrode position, and the practical difficulties of chronic implantation. This paper presents a novel model to investigate the information content about voluntary movement, based on the signals from active nerves. Differing from the myoelectric methods, this investigation provides a simulation of the …