Feature Extraction from EMG Signals for Artificial Neural Network Recognition

Before performing artificial neural network recognition on electromyography (EMG) signals, feature values must first be extracted from the signals. This process typically involves multiple methods to capture different aspects of the signal characteristics, such as time-domain features (like mean absolute value, root mean square, and variance calculated using functions such as mean(), rms(), and var() in MATLAB), frequency-domain features (including power spectral density analysis using pwelch() or fft() functions), and wavelet transform features (employing wavelet decomposition functions like wavedec() for multi-resolution analysis). Additionally, feature normalization using techniques such as z-score normalization (zscore() function) or min-max scaling is commonly applied to improve compatibility with artificial network training and prediction algorithms. The feature extraction stage represents a critical component in the EMG recognition pipeline, significantly influencing the accuracy and reliability of the entire classification process. Proper implementation typically involves creating feature extraction functions that return standardized feature vectors compatible with neural network input layers.