Abstract
There are many ways to control robotic devices. The modern and actual technology of brain-computer interfaces (BCI) is one of the ways to implement a control channel. This way provides additional control options for the operator, such as using facial expressions, mental activity and head movements. However, BCI is far from being used in everyday life conditions in part because of the influence of various noises that provoke changes in the EEG signal, called artifacts. Therefore, it is necessary to evaluate the impact of these noises on control commands executed using BCI and take this impact into account during the control commands design. This is especially true when several commands, that require various types of movements (facial expressions, head movements), are used in a single control configuration, since these commands themselves provoke such noises and guarantee their occurrence in the control process. In this paper, we considered a set of BCI commands for control a robotic wheelchair and propose a system of metrics for assessing the impact of artifacts on these commands. The system of metrics was used to assess the mutual impact of commands to each other and avoid conflicts of commands arising from the occurrence of artifacts.
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References
Voznenko, T.I., Chepin, E.V., Urvanov, G.A.: The control system based on extended BCI for a robotic wheelchair. Procedia Comput. Sci. 123, 522–527 (2018)
Nurseitov, D., Serekov, A., Shintemirov, A., Abibullaev, B.: Design and evaluation of a P300-ERP based BCI system for real-time control of a mobile robot. In: 2017 5th International Winter Conference on Brain-Computer Interface (BCI), pp. 115–120. IEEE, Sabuk, South Korea (2017)
Dardona, T., Eslamian, S., Reisner, L.A., Pandya, A.: Remote presence: development and usability evaluation of a head-mounted display for camera control on the da Vinci Surgical System. Robotics 8(2), 31 (14 pp.) (2019)
Ma, W., Tran, D., Le, T., Lin, H., Zhou, S.-M.: Using EEG artifacts for BCI applications. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 3628–3635. IEEE, Beijing, China (2014)
Cherepanova, A.D., Petrova, A.I., Voznenko, T.I., Dyumin, A.A., Gridnev, A.A., Chepin, E.V.: The research of distracting factors influence on quality of brain-computer interface usage. In: Samsonovich A. (eds.) Biologically Inspired Cognitive Architectures 2018 (BICA 2018), Advances in Intelligent Systems and Computing, vol. 848, pp. 44–49. Springer, Cham. (2018)
Voznenko, T.I., Dyumin, A.A., Aksenova, E.V., Gridnev, A.A., Delov, V.A.: The experimental study of ‘unwanted music’ noise pollution influence on command recognition by brain-computer interface. Procedia Comput. Sci. 123, 528–533 (2018)
Lotte, F.: A tutorial on EEG signal processing techniques for mental state recognition in brain-computer interfaces. In: Miranda, E.R., Castet, J. (eds.) Guide to Brain-Computer Music Interfacing, pp. 133–161. Springer, London (2014)
Samek, W., Müller, K.-R.: Tackling noise, artifacts and nonstationarity in BCI with robust divergences. In: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 2741–2745. IEEE, Nice, France (2015)
McFarland, D., Sarnacki, W., Vaughan, T., Wolpaw, J.: Brain-computer interface (BCI) operation: signal and noise during early training sessions. Clin. Neurophysiol. 116(1), 56–62 (2005)
Gulyaev, S.A., Arknipenko, I.V.: Artifacts in an electroencephalographic study: identification and differential diagnosis (Artefakty pri electroencephalographicheskom issledovanii: vyyavlenie i differencialniy diagnoz) (in Russian). Russkiy zhournal detskoy nevrologii 7(3), 3–16 (2012)
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Petrova, A.I., Voznenko, T.I., Chepin, E.V. (2020). The Impact of Artifacts on the BCI Control Channel for a Robotic Wheelchair. In: Misyurin, S., Arakelian, V., Avetisyan, A. (eds) Advanced Technologies in Robotics and Intelligent Systems. Mechanisms and Machine Science, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-33491-8_12
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DOI: https://doi.org/10.1007/978-3-030-33491-8_12
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