Classification of gait motor imagery while standing based on electroencephalographic bandpower

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Abstract

Brain-computer interfaces (BCIs) translate brain signals into commands for a device. BCIs are a complementary option in therapy during gait rehabilitation. This paper presents a strategy based on electroencephalographic (EEG) bandpower for detecting gait motor imagery (MI) while being standing. In particular, µ (8–13 Hz) and 20–35 Hz bands were used. Preliminary results show that two out of three users could achieve an accuracy above 70% of correct classifications. The proposed strategy could be used in a MI-based BCI to enhance brain activity associated to the gait process.

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APA

Angulo-Sherman, I. N., Rodríguez-Ugarte, M., Iáñez, E., & Azorín, J. M. (2017). Classification of gait motor imagery while standing based on electroencephalographic bandpower. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10338 LNCS, pp. 61–67). Springer Verlag. https://doi.org/10.1007/978-3-319-59773-7_7

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