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Lower-limb kinematic reconstruction during pedaling tasks from EEG signals using Unscented Kalman filter.
- Source :
-
Computer Methods in Biomechanics & Biomedical Engineering . May2024, Vol. 27 Issue 7, p867-877. 11p. - Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10255842
- Volume :
- 27
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- Computer Methods in Biomechanics & Biomedical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 176862439
- Full Text :
- https://doi.org/10.1080/10255842.2023.2207705