## 3 Challenges in Electromyography
### Signal Characteristics  
The muscle is responsible for the movement of joints in the human body and the force it can generate is usually proportional to its cross-sectional area. One of its main characteristics is plasticity, i.e., the ability to adapt to stimuli and modify its structure to meet functional demands [34]. The muscle fibers of a singular motor unit contract synchronously, resulting in the manifestation of a uniform motor unit potential. Within the muscle, fibers contract in small clusters simultaneously, necessitating the arrival of stimuli from the central nervous system or an external electrical stimulator. These stimuli propagate along the motor neurons axon until reaching the fibers of the motor unit. Upon innervation of each fiber, two depolarization zones emerge and propagate towards the fiber's ends at a velocity of 3-5 m/s. As the action potential traverses the fiber, it generates an electric field that disperses into the surrounding conducting medium, generating detectable voltages [35]. These voltages can be captured either via needle electrodes or on the skin's surface using surface electrodes.  
Due to variations in innervation points and conduction velocities among the fibers within a motor unit, the sources of the electric field are not perfectly aligned in space, resulting in asynchronous arrival times at the recording electrodes. Each descending impulse originating from the motor neuron and traveling along the axon triggers simultaneous contraction of the muscle fibers within the same motor unit.  
A growing body of evidence suggests that movements that appear fluid to the naked eye are actually composed of the temporal and spatial superposition of discrete sub-movements that are precisely recruited and coordinated by the central nervous system [36]. However, the spatial and temporal superposition of sub-movements has generally made it difficult to isolate the effects of individual sub-movements [37].  
The detection of non-stationarity in sEMG and kinematic variables is necessary to identify the onset of temporally overlapping sub-movements. It is therefore relevant to know how motor neurons that transmit electrical impulses to muscle fibers, causing them to contract, involve non-stable impulses that cause undetectable action potentials. The electrical activity that spreads along the muscle fibers, in fact, reflects the sum of numerous MUAPs, each of which contributes to the overall complexity and variability of the signal.  
Mathematically, the EMG signal can be modeled as a stochastic process characterized by deterministic and random components [38].  
The deterministic part is associated with the repetitive launch patterns of the motor units, while the random component arises from the asynchronous launch and the variability of the recruitment rates of the engine units. The frequency domain analysis of EMG signals is crucial to understanding muscle fatigue, neuromuscular disorders, and muscle activation efficiency. Techniques such as Fourier Transform, Wavelet Transform, and PCA/ICA are commonly used to break down biopotential signals (i.e., ECG, EEG, EMG) into their constituent frequencies, providing detailed spectral analysis [39, 40, 41].  
Modern signal processing techniques have significant potential to improve the quality and interpretability of EMG signals. Adaptive filtering, for example, can be used to dynamically remove noise and artifacts, improving the signal-noise ratio [42]. Furthermore, machine learning algorithms, convolutional neural networks (CNNs), have shown promise in the identification and classification of patterns within EMG signals. These algorithms can effectively manage the non-stationary nature of EMG signals and mitigate cross-talk effects, providing more accurate and reliable analysis.