review-article

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

Authors:

Sutrisno Ibrahim,

Wonsuk KoAuthors Info & Claims

Intelligent Service Robotics, Volume 13, Issue 4

Pages 539 - 563

https://doi.org/10.1007/s11370-020-00328-5

Published: 01 October 2020 Publication History

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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Cited By

Tunnell MChung HChang Y(2022)A Novel Convolutional Neural Network for Emotion Recognition Using Neurophysiological Signals2022 International Conference on Robotics and Automation (ICRA)10.1109/ICRA46639.2022.9811868(792-797)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1109/ICRA46639.2022.9811868

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

Index terms have been assigned to the content through auto-classification.

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cover image Intelligent Service Robotics

Intelligent Service Robotics Volume 13, Issue 4

Oct 2020

120 pages

ISSN:1861-2776

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© Springer-Verlag GmbH Germany, part of Springer Nature 2020.

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Springer-Verlag

Berlin, Heidelberg

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Published: 01 October 2020

Accepted: 27 May 2020

Received: 26 October 2018

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Deanship of Scientific Research at King Saud University in Riyadh, Saudi Arabia

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Tunnell MChung HChang Y(2022)A Novel Convolutional Neural Network for Emotion Recognition Using Neurophysiological Signals2022 International Conference on Robotics and Automation (ICRA)10.1109/ICRA46639.2022.9811868(792-797)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1109/ICRA46639.2022.9811868

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