1. Lower-limb kinematic reconstruction during pedaling tasks from EEG signals using Unscented Kalman filter.
- Author
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Blanco-Díaz CF, Guerrero-Mendez CD, Delisle-Rodriguez D, de Souza AF, Badue C, and Bastos-Filho TF
- Subjects
- Humans, Biomechanical Phenomena, Lower Extremity physiology, Bicycling physiology, Brain-Computer Interfaces, Male, Signal Processing, Computer-Assisted, Adult, Algorithms, Young Adult, Electroencephalography
- 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.
- Published
- 2024
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