A stochastic Bayesian nonlinear filtering system and method that improves the filtering of noisy signals by providing efficiency, power, speed, and flexibility. The filter only requires the likelihood function p(observationstate) to determine the system state and works in various measurement models. This allows for the processing of noisy signals to be used in real time, such as in a biofeedback device that senses noisy surface electomyography muscle electrical activity, filters the sensed signal using the nonlinear filtering method, and provides vibrations based on the muscular activity.
