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

Autor:	Blanco-Díaz CF; Postgraduate Program in Electrical Engineering, Federal University of Espírito Santo (UFES), Vitória, Brazil., Guerrero-Mendez CD; Postgraduate Program in Electrical Engineering, Federal University of Espírito Santo (UFES), Vitória, Brazil., Delisle-Rodriguez D; Edmond and Lily Safra International Institute of Neurosciences, Macaíba, Brazil., de Souza AF; Department of Informatics, Federal University of Espírito Santo (UFES), Vitória, Brazil., Badue C; Department of Informatics, Federal University of Espírito Santo (UFES), Vitória, Brazil., Bastos-Filho TF; Postgraduate Program in Electrical Engineering, Federal University of Espírito Santo (UFES), Vitória, Brazil.
Jazyk:	angličtina
Zdroj:	Computer methods in biomechanics and biomedical engineering [Comput Methods Biomech Biomed Engin] 2024 May; Vol. 27 (7), pp. 867-877. Date of Electronic Publication: 2023 May 02.
DOI:	10.1080/10255842.2023.2207705
Abstrakt:	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.
Databáze:	MEDLINE
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