Title: Classification improvement using an unscented Kalman filter in brain computer interface systems
Authors: Wansu Lim; Yeon-Mo Yang
Addresses: Department of Electronic Engineering, Kumoh National Institute of Technology, Gumi, Gyeongbuk, 730-701, South Korea ' Department of Electronic Engineering, Kumoh National Institute of Technology, Gumi, Gyeongbuk, 730-701, South Korea
Abstract: In this paper, we propose an enhanced classification technique using an unscented Kalman filter (UKF) for brain computer interface (BCI) signal processing. Since the UKF estimates the state of a nonlinear dynamic system and parameters for nonlinear system identification, the UKF can significantly improve the performance of classification in BCI systems. As a result, we confirm the performance improvement when using the UKF in motor imagery classification in terms of accuracy, Kappa value, and confidence interval.
Keywords: brain computer interface; BCI; unscented Kalman filter; UKF; classification; statistical signal processing.
International Journal of Computational Vision and Robotics, 2017 Vol.7 No.6, pp.723 - 729
Received: 17 Jul 2015
Accepted: 02 Aug 2015
Published online: 24 Jul 2017 *