Classification improvement using an unscented Kalman filter in brain computer interface systems
by Wansu Lim; Yeon-Mo Yang
International Journal of Computational Vision and Robotics (IJCVR), Vol. 7, No. 6, 2017

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.

Online publication date: Mon, 24-Jul-2017

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