Improved detection rate in motor imagery based BCI systems using combination of robust analytic phase and envelope features

Abstract

Motor imagery based brain-computer interface (BCI) systems translate the motor-intention of an individual into a control signal. For this BCI system, most of previous studies are based on power changes of mu and beta rhythms. In this paper, we employ both phase and envelope features of EEG signals to cover a comprehensive set of required information for intention detection. For this purpose we use narrow-band channelization in combination with a recently proposed Monte Carlo based statistical approach for EEG instantaneous parameters estimation known as transfer function perturbation (TFP) to calculate time/frequency measures of these parameters. The estimated time/frequency measures of instantaneous envelope (IE) and instantaneous phase (IP) using TFP are then utilized to elicit two set of robust features containing Shannon entropy of IE and phase lag index of IP. Extracted features are trained and tested by a KNN classifier to discriminate between classes in a three class motor imagery based BCI system. Results show that the combination of proposed robust analytic phase and envelope features outperforms either solely phase or envelope features and improves the classification rate in three class BCI problems.