Abstract
The combination of a lower-limb exoskeleton with brain computer interfaces (BCI) can assist patients with motor impairment to walk again. In addition, it can promote the neural plasticity of the affected brain region. The present paper shows a research performed on seven able-bodied subjects that walked with an assistive exoskeleton controlled by external commands. The main objective was to identify in which frequency band the differences between periods of motor imagery and rest were more evident. The comparison was done with different classifiers and the results reveal that for the majority of them, the frequency band of 14–19 Hz provided the highest accuracy.
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T. Nierhaus, C. Vidaurre, A. Villringer, Immediate brain plasticity after one hour of brain–computer interface (BCI). J. Physiol. 17 (2020)
P. Wierzgała, D. Zapała, G.M. Wojcik, J. Masiak, Most popular signal processing methods in motor-imagery BCI: a review and meta-analysis. Front. Neuroinform. 12(November), 1–10 (2018)
G. Pfurtscheller, C. Brunner, A. Schlo, F.H. Lopes, Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31, 153–159 (2006)
M. Ortiz, E. Iáñez, J. Gaxiola, A. Kilicarslan, J.M. Azorín, S. Member, Assessment of motor imagery in gamma band using a lower limb exoskeleton, in IEEE International Conference on Systems, Man and Cybernetics (SMC) (2019), pp. 2773–2778
A. Kilicarslan, R.G. Grossman, J.L. Contreras-vidal, A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements. J. Neural Eng. 13(2), 26013 (2016)
Á. Costa et al., Decoding the attentional demands of gait through EEG gamma band features. PLoS One (2016)
R.G. Stockwell, R.P. Lowe, L. Mansinha, Localization of the complex spectrum: the S transform. IEEE Trans. Signal Process. (1996)
J.S. Kirar, R.K. Agrawal, Relevant frequency band selection using sequential forward feature selection for motor imagery brain computer interfaces, in 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (2018), pp 52–59
Acknowledgements
This research was funded by the Spanish Ministry of Science, Innovation and Universities through grant CAS18/00048 ‘José Castillejo’; by the Spanish Ministry of Science, Innovation and Universities, the Spanish State Agency of Research, and the European Union through the European Regional Development Fund in the framework of the project Walk—Controlling lower-limb exoskeletons by means of brain-machine interfaces to assist people with walking disabilities (RTI2018-096677-B-I00); and by the Consellería de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana) and the European Social Fund in the framework of the project ‘Desarrollo de nuevas interfaces cerebro-máquina para la rehabilitación de miembro inferior’ (GV/2019/009).
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Ferrero, L. et al. (2022). Comparison of Different Brain–Computer Interfaces to Assess Motor Imagery Using a Lower-Limb Exoskeleton. In: Torricelli, D., Akay, M., Pons, J.L. (eds) Converging Clinical and Engineering Research on Neurorehabilitation IV. ICNR 2020. Biosystems & Biorobotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-030-70316-5_9
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DOI: https://doi.org/10.1007/978-3-030-70316-5_9
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