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Comprehensive review on brain-controlled mobile robots and robotic arms based on electroencephalography signals

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Abstract

There is a significant progress in the development of brain-controlled mobile robots and robotic arms in the recent years. New advances in electroencephalography (EEG) technology have led to the possibility of controlling external devices, such as robots, directly via the brain. The development of brain-controlled robotic devices has allowed people with bodily disabilities to enhance their mobility, individuality, and many types of activity. This paper provides a comprehensive review of EEG signal processing in robot control, including mobile robots and robotic arms, especially based on noninvasive brain computer interface systems. Various filtering approaches, feature extraction techniques, and machine learning algorithms for EEG classification are discussed and summarized. Finally, the conditions of the environments in which robots are used and robot types are also discussed.

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Acknowledgements

The authors acknowledge the College of Engineering Research Center and Deanship of Scientific Research at King Saud University in Riyadh, Saudi Arabia, for the financial support to carry out the research work reported in this paper.

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  1. Electrical Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia

    Majid Aljalal, Ridha Djemal & Wonsuk Ko

  2. Electrical Engineering Department, College of Engineering, Sebelas Maret University, Surakarta, Indonesia

    Sutrisno Ibrahim

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Aljalal, M., Ibrahim, S., Djemal, R. et al. Comprehensive review on brain-controlled mobile robots and robotic arms based on electroencephalography signals. Intel Serv Robotics 13, 539–563 (2020). https://doi.org/10.1007/s11370-020-00328-5

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