Stochastic Generative Model and Signal Analysis of Surface EMG

Surface electromyography (EMG) signals generated during muscle contraction contain various information related to human motion intention and muscle contraction, and are widely used for robotic prosthetic hand control and diagnosis of neuromuscular diseases. EMG signals have traditionally been assumed to follow a zero-mean Gaussian distribution, and various signal processing and analysis methods have been developed based on this assumption. However, recent studies have experimentally reported that EMG signals can exhibit non-Gaussian properties due to changes in muscle activity characteristics such as changes in muscle contraction level and the presence of muscle fatigue. Therefore, conventional approaches based on Gaussian distribution models cannot adequately handle the features contained in EMG signals. In this research, we assume that the recently reported non-Gaussian nature of EMG signals is caused by uncertainty in the amplitude (=variance) of EMG signals, and we are working on creating new signal analysis methods by constructing stochastic generative models that can express this.