Influences of the Trained State Model into the Decoding of Elbow Motion Using Kalman Filter

Abstract

The properties of the Kalman Filter to decode elbow movement from non-invasive EEG are analyzed in this article. A set of configuration parameters using cross-validation are tested in order to find the ones that reduce the estimation error. Found that selecting correctly the number of channels and the time step used to configure the signal, it is possible to improve the filter estimation capabilities. As there was an apparent incidence of the variations in the recorded data used to train the model, an investigation of how those alterations affect the estimation precision in various data sets was made. The presented results showed that significant variations in the velocity and acceleration of the data set trains filters with lower accuracy than the ones built from a more uniform set.