This paper provides a comprehensive overview of the methodologies and algorithms used for the analysis of Electromyography (EMG) signals, which are crucial for clinical applications, Evolvable Hardware Chip (EHW) development, and human-computer interaction. The authors discuss various techniques for detecting, decomposing, processing, and classifying EMG signals, including wavelet transform, time-frequency approaches, Fourier transform, Wigner-Ville Distribution (WVD), statistical measures, and higher-order statistics. They also explore the use of Artificial Intelligence (AI) techniques such as Artificial Neural Networks (ANN), dynamic recurrent neural networks (DRNN), and fuzzy logic systems. The paper highlights the importance of understanding the nature and characteristics of EMG signals to develop more powerful, flexible, and efficient applications. Additionally, it presents hardware implementations of EMG signals for prosthetic hand control, grasp recognition, and human-machine interaction, and includes a comparison study to evaluate the performance of different EMG signal analysis methods. The authors aim to provide researchers with a thorough understanding of EMG signal analysis, enabling them to develop advanced applications in the field.This paper provides a comprehensive overview of the methodologies and algorithms used for the analysis of Electromyography (EMG) signals, which are crucial for clinical applications, Evolvable Hardware Chip (EHW) development, and human-computer interaction. The authors discuss various techniques for detecting, decomposing, processing, and classifying EMG signals, including wavelet transform, time-frequency approaches, Fourier transform, Wigner-Ville Distribution (WVD), statistical measures, and higher-order statistics. They also explore the use of Artificial Intelligence (AI) techniques such as Artificial Neural Networks (ANN), dynamic recurrent neural networks (DRNN), and fuzzy logic systems. The paper highlights the importance of understanding the nature and characteristics of EMG signals to develop more powerful, flexible, and efficient applications. Additionally, it presents hardware implementations of EMG signals for prosthetic hand control, grasp recognition, and human-machine interaction, and includes a comparison study to evaluate the performance of different EMG signal analysis methods. The authors aim to provide researchers with a thorough understanding of EMG signal analysis, enabling them to develop advanced applications in the field.