Electromyography Signal

Electromyography (EMG) signal analysis focuses on extracting meaningful information from electrical activity in muscles to understand movement, intention, and even health conditions. Current research emphasizes developing robust and accurate methods for classifying EMG signals using diverse machine learning approaches, including deep convolutional neural networks, recurrent neural networks (like LSTMs), and novel architectures like transformers and physics-informed neural networks, often combined with advanced signal processing techniques such as canonical correlation analysis. These advancements are driving improvements in applications such as prosthetic control, brain-computer interfaces, and the diagnosis of neuromuscular disorders and sleep-related conditions, offering the potential for more effective and personalized healthcare solutions.