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

Cristian Felipe Blanco-Díaz, Cristian David Guerrero-Mendez, Denis Delisle-Rodriguez, Alberto Ferreira de Souza, Claudine Badue and Teodiano Freire Bastos-Filho

Computer Methods in Biomechanics and Biomedical Engineering, 2024, vol. 27, issue 7, 867-877

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.

Date: 2024
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DOI: 10.1080/10255842.2023.2207705

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