1. Gopal P., Gesta A., Mohebbi A. A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models // Sensors, 2022, V. 22. № 10. 3650. https://doi.org/10.3390/s22103650.
2. Voznenko T.I., Gridnev A.A., Kudryavtsev K.Y., Chepin E.V. The Decomposition Method of Multi-channel Control System Based on Extended BCI for a Robotic Wheelchair // Advances in Intelligent Systems and Computing, 2020. V. 948. P. 562-567. https://doi.org/10.1007/978-3-030-25719-4_73.
3. Han J.S., Song W.K., Kim J.S., Bang W.C., Lee H., Bien Z. New EMG pattern recognition based on soft computing techniques and its application to control a rehabilitation robotic arm // Proc. of 6th international conference on soft computing (IIZUKA2000), 2000. P. 890-897.
4. Лобов С.А., Миронов В.И., Кастальский И.А., Казанцев В.Б. Совместное использование командного и пропорционального управления внешними робототехническими устройствами на основе электромиографических сигналов // Современные технологии в медицине. 2015. T. 7. № 15. С. 30-38.
5. Reifinger S., Wallhoff F., Ablassmeier M., Poitschke T., Rigoll G. Static and Dynamic Hand-Gesture Recognition for Augmented Reality Applications // Human-Computer Interaction. HCI Intelligent Multimodal Interaction Environments, 2007. V. 4552. P. 728-737. https://doi.org/10.1007/978-3-540-73110-8_79.
6. Исмайылова К.Ш. Факторы, влияющие на искажение измерительной информации в электромиографии // Наука, техника и образование. 2017. №10. С. 21-23.
7. Zhang X., Huang H. A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition // Journal of neuroengineering and rehabilitation, 2015. V. 12. P. 1-16. https://doi.org/10.1186/s12984-015-0011-y.
8. Phinyomark A., Phukpattaranont P., Limsakul C. Feature reduction and selection for EMG signal classification // Expert systems with applications, 2012. V. 39. № 8. Р. 7420-7431. https://doi.org/10.1016/j.eswa.2012.01.102.
9. Gridnev A.A., Voznenko T.I., Chepin E.V. The decision-making system for a multi-channel robotic device control // Procedia computer science, 2018. V. 123, Р. 149-154. https://doi.org/10.1016/j.procs.2018.01.024.
10. Kim D., Jung H., Shin S. System and method of controlling mobile robot using inertia measurement unit and electromyogram sensor-based gesture recognition. Patent KR. No. 20170030139, 2015.
11. Petrova A.I., Voznenko T.I., Chepin E.V. The Impact of Artifacts on the BCI Control Channel for a Robotic Wheelchair // Mechanisms and Machine Science (book series), 2020. V. 80, Р. 105-111. https://doi.org/10.1007/978-3-030-33491-8_12.