Paper Title :
Frequency Domain Analysis Of Electromyography Signals From Facial Muscles With Neural Networks
EMG signal is a complicated signal, which is controlled by the nervous system. Quantitative analysis in clinical electromyography (EMG) is very desirable because it allows a more standardized, sensitive and specific evaluation of the neurophysiologic findings, especially for the assessment of neuromuscular disorders.. In this study, we have investigated that, The analysis of different electromyography signals (NOR & MYO). This paper basically deals with the basic steps for recording, analysis of EMG signal,. For recording of EMG of a muscles or facial muscles two electrodes are used one is surface electrodes and second one is needle electrode, after comparing both electrodes we found surface electrode is better than needle electrode .The analysis the EMG signal during three phase segmentation, classification and feature extraction. We extracted both time domain (TPDs) and frequency domain parameters (FDPs),by which we get some important information of MUAP abnormality and muscular change. and we also concluded its best application for recognition of Facial Expression . Facial expression analysis is rapidly becoming an area of intense interest in computer science and human-computer interaction design communities. The most expressive way humans display emotions is through facial expressions.