## 1 Introduction  
Signals are the backbone of science, technology, and communication, serving as the fundamental carriers of information that enable processes and facilitate understanding. At their core, signals transmit messages through changing patterns, amplitudes, and frequencies over time. In the realm of deep learning, accurately modeling these signals is critical, as it requires identifying the key factors of each algorithm and understanding the relationships between different signals and features. This understanding is crucial for making accurate predictions. Surface Electromyography (SEMG) based Hand Gesture Recognition (HGR) has shown limitations due to individual variability in EMG signals [1]. Each person's unique EMG signal necessitates personalized HGR systems. Recent studies highlight the potential of fusing SEMG with electroencephalography (EEG) signals to enhance the flexibility and accuracy of arm motion recognition. This fusion, facilitated by advanced techniques like Graph Convolutional Networks (GCNs) and Functional Connectivity, establishes a deeper link between brain signals (EEG) and motor neuron signals (SEMG) [2]. Technological advancements now allow for the non-invasive capture of high-definition SEMG signals with minimal noise, providing new insights for neuroscientists. These signals are integral to Human-Machine Interfaces (HMI) [3], which rely on the brain's bio-signal processing and represent the future of biocybernetics. For individuals without hands, EMG technology plays a pivotal role by enabling the control of prosthetic limbs or assistive devices through muscle activity detection. This technology translates muscle signals into meaningful commands, allowing for the precise control of prosthetic hands, thereby enhancing autonomy and quality of life. This research aims to harness advanced signal feature extraction methodologies and machine learning algorithms to analyze the GRABMyo dataset, which contains unique EMG signals from the forearm. The dataset includes three sessions with 43 participants, each performing 16 gestures, captured seven times per gesture. By employing signal processing techniques such as Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossing (ZC), Slope Sign Change (SSC), Root Mean Square (RMS), Energy of Wavelet Coefficients (EWLs), and Enhanced Mean Absolute Value (EMAV), this study seeks to uncover nuanced patterns within the EMG signals indicative of forearm muscle activity. Dimensionality reduction techniques like Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) will refine the extracted features, enhancing their discriminative power. Machine learning models will then be used to predict various forearm muscle movements based on these features, potentially advancing the development of intuitive prosthetic control systems and rehabilitation technologies. The research will involve preprocessing raw EMG signals, decomposing and reconstructing them using the Biorthogonal 3.3 Discrete Wavelet Transform (DWT), and implementing feature extraction and dimensionality reduction. Finally, classification algorithms will predict specific hand gestures from the optimized dataset, focusing on five primary gestures: Little Finger Extension (LFE), Index Finger Extension (IFE), Thumb Extension (TE), Hand Open (HO), and Hand Close (HC). Despite potential limitations, such as the quality of the dataset and variability in EMG signals, this research aims to enhance our understanding of forearm muscle signals and improve the accuracy of gesture predictions, ultimately contributing to the advancement of assistive technologies.