Abstract
The study describes an approach for supervisory control of a limb prosthesis and a mobile robot based on a non-invasive brain-computer interface. Key applications of the system are the maintenance of immobilized patients and rehabilitation procedures. An interface performs imaginary hand movement decoding using electroencephalographic signals. The decoding process consists of several steps: (1) signal acquisition; (2) signal preprocessing (filtering, artefact removal); (3) feature extraction; (4) classification. The study is focused on obtaining the best accuracy of decoding by comparing different feature extraction and classification methods. Several methods (Riemannian geometry-based) were tested offline. Furthermore, online testing of control capabilities using in-house data was performed.
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The work was financially supported by RFBR grant 16-29-08296.
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Kapralov, N.V., Ekimovskii, J.V., Potekhin, V.V. (2020). EEG-Based Brain-Computer Interface for Control of Assistive Devices. In: Arseniev, D., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds) Cyber-Physical Systems and Control. CPS&C 2019. Lecture Notes in Networks and Systems, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-030-34983-7_52
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