FREQUENCY DOMAIN ANALYSIS OF ELECTROMYOGRAPHY SIGNALS FROM FACIAL MUSCLES WITH NEURAL NETWORKS

EMG signal is a complicated signal, which iscontrolled by the nervous system. Quantitative analysis in clinicalelectromyography (EMG) is very desirable because it allows amore standardized, sensitive and specific evaluation of theneurophysiologic findings, especially for the assessment ofneuromuscular disorders.. In this study, we have investigatedthat, The analysis of different electromyography signals (NOR &MYO). This paper basically deals with the basic steps forrecording, analysis of EMG signal,. For recording of EMG of amuscles or facial muscles two electrodes are used one is surfaceelectrodes and second one is needle electrode, after comparingboth electrodes we found surface electrode is better than needleelectrode .The analysis the EMG signal during three phasesegmentation ,classification and feature extraction. We extractedboth time domain (TPDs) and frequency domain parameters(FDPs),by which we get some important information of MUAPabnormality and muscular change. and we also concluded its bestapplication for recognition of Facial Expression . Facialexpression analysis is rapidly becoming an area of intense interestin computer science and human-computer interaction designcommunities. The most expressive way humans display emotionsis through facial expressions.