# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
## 2 State of the Art  
To acquire an EMG signal, a relatively simplex system is required that functions both as a signal amplifier and as a processing unit. The signal amplifier boosts the weak electrical signals generated by the muscles, as these signals typically have low amplitude and are highly affected by noise [7; 8]. Needle electrodes or intramuscular electrodes (iEMG), due to their direct contact with muscle fibers (invasive), offer more precise electrical measurements with reduced noise and interference compared to surface electrodes. While iEMG are less affected by external noise and movement artifacts, surface electrodes (sEMG), being non-invasive, are more prone to these issues and capture signals from broader areas, which lowers the signal-to-noise ratio [14]. Once amplified, the signals must be processed to filter out this unwanted noise, enhance relevant frequencies, and convert the analog signals into digital formats for further analysis. The processing unit then interprets these cleaned signals to assess muscle activity accurately, enabling real-time monitoring and control of muscle movement. Processing EMG signals involves multiple steps, from preprocessing and feature extraction to real-time signal classification and integration into control systems. Each step contributes to enhancing the accuracy, efficiency, and usability of EMG-based applications, particularly in prosthetics and rehabilitation technologies [7; 8; 15]. 1. Preprocessing (Signal Conditioning): preprocessing ensures that EMG signals are clean and ready for analysis. Key processes include the following: - Amplification and Filtering: weak signals are amplified and processed using high-pass and low-pass filters to limit the signal bandwidth and remove noise or aliasing effects. A notch filter is employed to eliminate power-line interference. - Full-wave rectification and smoothing: an overall trend of muscle activity is obtained using an envelope detector. 2. Feature Extraction Common methods include the following: - Time-domain features: Mean Absolute Value (MAV), Root Mean Square (RMS), Zero-Crossing Rate (ZCR), and Slope Sign Changes (SSC), which describe signal magnitude and dynamics. - Frequency-domain features: metrics such as Median Frequency (MDF), Mean Power Frequency (MPF), and Power Spectral Density (PSD) are used to detect muscle fatigue and activity. - Wavelet Transform: Provides detailed time-frequency analysis, offering a multi-scale view of muscle activity. 3. Signal Classification: Processed signals are interpreted for real-time control, often using Supervised or Unsupervised Learning Techniques. 4. Real-Time Processing: Critical for applications such as prosthetics or exoskeletons. 5. Signal Integration (Control Systems): Once processed, EMG signals are integrated into control systems to operate external devices: - Command Generation: Signals are translated into specific actions, such as controlling prosthetic or robotic actuators. - Feedback Systems: Device feedback adjusts user movements and enhances system responsiveness and accuracy. Nowadays, EMG systems can be classified on the following:
* Single Amplification and Filtering Systems: The simplest devices available. Consist of sEMG or iEMG electrodes connected to amplifiers and filters. They can supportmultiple wired channels to acquire signals from different locations using at least three electrodes, where one is used as a reference and the other two as a differential channel. * High-Density EMG (HDEMG) Systems: Capture high-density EMG signals using more electrodes, enabling higher resolution by recording from multiple muscle sites simultaneously. While well-suited for research and diagnostics, they generate large volumes of data, which increase complexity, computational demands, and cost, making them ideal primarily for specific studies in controlled environments.