Document Type



Master of Science (MS)


Electrical Engineering

First Advisor's Name

Dong C. Park

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Wunnava Subbarao

Third Advisor's Name

Malcolm L. Heimer

Fourth Advisor's Name

James R. Story


Electromyography, Neural networks (Computer science)

Date of Defense



Application of EMG-controlled functional neuromuscular stimulation to a denervated muscle depends largely on the successful discrimination of the EMG signal by which the subject desires to execute control over the impeded movement. This can be achieved by an adaptive and flexible interface regardless of electrodes location, strength of remaining muscle activity or even personal conditions. Adaptability is a natural and important characteristic of artificial neural networks. This research work is restricted to the development of a real-time application of artificial neural network to the EMG signature recognition. Through this new approach, EMG features extracted by Fourier analysis are presented to a multilayer perceptron type neural network. The neural network learns the most relevant features of the control signal. For real-time operation, a digital signal processor operates over the resulting set of weights from the learning process, and maps the incoming signal to the stimulus control domain. Results showed a highly accurate discrimination of the EMG signal over interference patterns.




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