Lower-limb kinematic reconstruction during pedaling tasks from EEG signals using Unscented Kalman filter

Abstract

Kinematic reconstruction of lower-limb movements using electroencephalography (EEG) has been used in several rehabilitation systems. However, the nonlinear relationship between neural activity and limb movement may challenge decoders in real-time Brain-Computer Interface (BCI) applications. This paper proposes a nonlinear neural decoder using an Unscented Kalman Filter (UKF) to infer lower-limb kinematics from EEG signals during pedaling. The results demonstrated maximum decoding accuracy using slow cortical potentials in the delta band (0.1-4 Hz) of 0.33 for Pearson’s r-value and 8 for the signal-to-noise ratio (SNR). This leaves an open door to the development of closed-loop EEG-based BCI systems for kinematic monitoring during pedaling rehabilitation tasks.

Keywords:

• EEG-based BCI
• kinematics reconstruction
• nonlinear neural decoder
• unscented Kalman filter
• pedaling rehabilitation

Acknowledgement(s)

The authors would like to thank Federal University of Espirito Santo (UFES/Brazil) and FAPES/I2CA (Resolution N ${}^{°}$ 285/2021) by the MSc scholarships awarded to the first two authors.

Disclosure statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Funding

This work was supported by Federal University of Espírito Santo (UFES/Brazil) and FAPES/I2CA (Resolution N° 285/2021) through the MSc. scholarships awarded to the first two authors.