A computational model and simulation study of the efferent activity in the brachial nerves during voluntary motor intent.

Inherent limitations of the surface myoelectric signal, such as the lack of recording sites in high-level amputations, and the sensitivity to placement and impedance effects, confound its wider application in powered prostheses. Since a functionally topographic distribution (somatotopic organization) of nerve fascicles exists within the peripheral nerves, it is theoretically possible that complete motor control information can be retrieved from peripheral nerve signals. In this study, we present a computational model that simulates the recording from specific nerve fascicles in the upper limb during voluntary contractions while they innervate relevant muscles. A procedure of classifying the nerve data is presented using a set of time domain features and a spike detection algorithm. Recommendations are made to achieve optimal neural signal recognition, with regard to electrode geometry and signal analysis.