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

Author

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

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.

Suggested Citation

  • Cristian Felipe Blanco-Díaz & Cristian David Guerrero-Mendez & Denis Delisle-Rodriguez & Alberto Ferreira de Souza & Claudine Badue & Teodiano Freire Bastos-Filho, 2024. "Lower-limb kinematic reconstruction during pedaling tasks from EEG signals using Unscented Kalman filter," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 27(7), pages 867-877, May.
    • Handle: RePEc:taf:gcmbxx:v:27:y:2024:i:7:p:867-877
    DOI: 10.1080/10255842.2023.2207705
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