Abstract
Brain Machine Interfaces (BMI) combined with lower-limb exoskeletons can assist patients that have difficulties in walking. However, BMI need some calibration to adjust their parameters to each user. This process is time-consuming and can be fatiguing for the users. In this work, the optimal number of recordings needed to adjust a EEG-based BMI to distinguish between MI of gait and rest state has been studied based on three subjects. The results show that the BMI reaches its highest accuracy with 5 recordings.
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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., Quiles, V., Ortiz, M., Iáñez, E., Contreras-Vidal, J.L., Azorín, J.M. (2022). Optimal Calibration Time for Lower-Limb Brain–Machine Interfaces. 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_10
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DOI: https://doi.org/10.1007/978-3-030-70316-5_10
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