Fitting Distribution for Electromyography and Electroencephalography Signals Based on Goodness-of-Fit Tests

Abstract

Physiological devices (PDs) like upright bicycles, steppers and treadmills act as Human Machine Interface (HMI) during rehabilitation. The main issue on how to utilize biosignals such as Electromyography (EMG), Electrocardiography (ECG) and Electroencephalography (EEG) as an inputs for HMI to control the PDs. Biosignals are stochastic and complex as they influenced by anatomical and physiological properties of muscles. To maximize the training time during exercise, the features of the biosignals (e.g., fatigue, contraction or relaxation) should be extracted to maintain the system as a reliable working condition. For that purpose, it is feasible to try out a probabilistic distribution as a feature to illustrate the pattern of muscle activation. Before the estimation of parameter distribution is conducted, we need to verify the types of distribution that fit the raw biosignals. In this study, EMG and EEG signals will be considered to find the most suitable distribution for the signals. The selected model is chosen based on a minimum error produced by two Goodness-of-Fit (GOF) tests namely Kolmogorov-Smirnov statistic, D and Anderson Darling statistic, A2. As a result, a Generalized Extreme Value (GEV) distribution is found the most appropriate distribution compared to Generalized Pareto (GP) and Exponential (EXP) distributions for describing the EMG and EEG signals.