Abstract
The work is devoted to the problems of development and application of brain–computer interfaces in contactless control systems for robotic devices. The brain–computer interfaces on the basis of classifiers of electroencephalographic signals arising from the imagination of various movements are considered. A description and comparison of existing classifiers is given and it is shown that they can provide an accuracy of 60–80% when recognizing up to 2–4 classes of movements. A new type of classifier based on a neuromorphic network is proposed, which showed a classification accuracy of at least 90% for 4 classes of imaginary commands. An example of the use of brain–computer interface to control a mobile robot is given.
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Acknowledgements
The work was carried out as the part of the state task of the Russian Ministry of Edu-cation and Science No. 075-01623-22-00 “Research and development of a biosimilar system for controlling the behavior of mobile robots based on energy-efficient soft-ware and hardware neuromorphic tools”.
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Gundelakh, F.V., Stankevich, L.A. (2023). Robotic Devices Control Based on Neuromorphic Classifiers of Imaginary Motor Commands. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research VI. NEUROINFORMATICS 2022. Studies in Computational Intelligence, vol 1064. Springer, Cham. https://doi.org/10.1007/978-3-031-19032-2_8
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