## 2 Literature Review  
The literature on machine learning-based feature extraction and classification of EMG signals for intuitive prosthetic control has expanded significantly in recent years, reflecting the growing interest in developing advanced assistive technologies. Traditional approaches to EMG signal analysis often relied on basic statistical methods and manual feature selection, which posed limitations in capturing the complex patterns inherent in EMG signals. Recent advancements have introduced more sophisticated techniques, such as time-frequency analysis and wavelet transform, to better characterize EMG signals. For instance, this work [3] emphasized the importance of feature extraction methods like Mean Absolute Value (MAV), Zero Crossing (ZC), and Root Mean Square (RMS) for enhancing signal interpretation. These methods, when combined with machine learning algorithms, have shown promising results in classifying various hand gestures and muscle movements. Machine learning [4; 5], particularly deep learning, has revolutionized EMG signal processing by automating feature extraction and improving classification accuracy. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been employed to learn spatial and temporal dependencies in EMG data, leading to significant improvements in gesture recognition tasks. This work [6] demonstrated that CNNs could effectively capture the spatial patterns of EMG signals, resulting in a higher classification performance compared to traditional methods. Similarly, the paper [7] utilized Support Vector Machines (SVMs) and Random Forests to classify EMG signals from the Ninapro database, achieving high accuracy and robustness. The integration of EMG with other bio signals, such as electroencephalography (EEG), has also been explored to enhance prosthetic control systems. Other research [8; 9; 10; 11] investigated the fusion of SEMG and EEG signals using Graph Convolutional Networks (GCNs) to improve the recognition of complex arm movements. This multi-modal approach leverages the complementary information provided by different types of bio signals, leading to more reliable and flexible control mechanisms. Additionally, feature selection and dimensionality reduction techniques, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), have been applied to optimize the features extracted from EMG signals, reducing computational complexity while maintaining high classification accuracy. The application of these advanced techniques has significant implications for the development of intuitive prosthetic control systems. By enabling a more accurate and responsive control of prosthetic limbs, these methods can substantially improve the quality of life for individuals with limb loss. Future research is likely to focus on further refining machine learning models, exploring new feature extraction methods, and integrating additional bio signals to enhance the functionality and usability of prosthetic devices. Overall, the convergence of machine learning and EMG signal processing represents a promising frontier in the field of assistive technology, with the potential to deliver more natural and effective prosthetic control solutions.  
Figure 1 depicts the comprehensive process diagram outlining the distinct phases of the research from Signal Processing to Machine Learning.  
A thorough exploration of the key concepts and methods applied will be addressed in the subsequent sections, namely Sections 3 and 4.