Current motorized limb prostheses provide rudimentary functionality for the application in everyday life. Together with a poor cosmetic appearance, this is the reason why a large percentage of amputees do not use their prosthetic device regularly. This paper seeks to present an overview of current state of the art research on neural interfaces. The focus lies on non-invasive recording with EMG and especially High-Density EMG sensors. Additionally, direct machine learning and pattern recognition algorithms for the decoding of the recorded signals are discussed. Finally, promising research directions for advanced prosthesis control will be discussed. The bionic arm uses EMG signals to control each action of the hand. In order to control them, we need to record the EMG signal for different actions. And compare it with real-time values to move the hand in a different manner. There are separate servo motors to control the actions of each finger separately. So these are programmed by using microcontrollers.