ISSN 2411-958X (Print) ISSN 2411-4138 (Online) European Journal of Interdisciplinary Studies May-August 2016 Volume 2, Issue 3 35 Comparison of Different Time and Frequency Domain Feature Extraction Methods on Elbow Gesture’s EMG Cemil Altın Orhan Er Bozok University,Electrical-Electronics Engineering, 66200, Yozgat, Turkey. Abstract Objective:In this study we will get EMG signals from arm for different elbow gestures, than filtering the signal and later classification the signal. The reason for doing is that, EMG signals are used for many rehabilitation and assistive prostheses of paralyzed or injured people. Methods:Filtering a biological signal is the key point for these type studies. Filtering the EMG signals needed and starts with the elimination of the 50 Hz mains supply noise. After filtering the signal, feature extraction will be applied for both wrist flexion and wrist extension cases. There are many feature extraction methods for time and frequency domain. After feature extraction, classification of hand movements will be studied using extracted features. Classification is made using K Nearest Neighbor algorithm. The dataset used in this study is acquired by the EMG signal acquisition tool and belong to us. Results:90 % accuracy performance is obtained by K Nearest Neighbor algorithm purposed signal classification. Conclusion:This system is capable of conducting the classification process with a good performance to biomedical studies. So,this structure can be helpful as machine-learning based decision support system for medical purpose. Keywords: Elbow Gesture’s EMG,Feature Extraction, Time and Frequency Domain. 1. Introduction EMG signal is one of the main signals produced by the human body especially by the muscles. Although the results of electromyography are nonspecific electromyography is very sensitive [1]. EMG signal is widely used in many applications recently. The most active area of this application is prosthesis hand control. EMG signal also used for human-machine interface. EMG has advantages compare to other biological signals. Because EMG signals are powerful and have high signal to noise ratio. 2. Theory 2.1. EMG Data Acquisition EMG signals are generated by the exchange of ions across the muscle membranes and detected with the help of electrodes as shown fig. 1. Figure 1. EMG acquisition from muscles [2]