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Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor

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2081-4836
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Towards Hybrid EEG-EMG-Based Control Approaches to be Used in Bio-robotics Applications: Current Status, Challenges and Future Directions

Thilina Dulantha Lalitharatne
  • Corresponding author
  • Dept. of Advanced Technology Fusion, Saga University, 1-Honjo machi, Saga-shi, Saga, 840-8502, Japan
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/ Kenbu Teramoto
  • Corresponding author
  • Dept. of Advanced Technology Fusion, Saga University, 1-Honjo machi, Saga-shi, Saga, 840-8502, Japan
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/ Yoshiaki Hayashi
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  • Dept. of Advanced Technology Fusion, Saga University, 1-Honjo machi, Saga-shi, Saga, 840-8502, Japan
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/ Kazuo Kiguchi
  • Corresponding author
  • Dept. of Mechanical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka-shi, Fukuoka, 819-0395, Japan
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Published Online: 2013-12-10 | DOI: https://doi.org/10.2478/pjbr-2013-0009

Abstract

In the last few decades, bio-robotics applications such as exoskeletons, prosthetics and robotic wheelchairs have progressed from machines in science fiction to nearly commercialized products. Though there are still several challenges associated with electromyography (EMG) signals, the advances in use of EMG signals for controlling such bio-robotics applications have been enormous. Similarly, recent trends and attempts in developing electroencephalography- (EEG) based control methods have shown the potential of this area in the modern bio-robotics field. However, the EEG-based control methods are also yet to be perfected. A new approach of combining both these control methods, which take the advantages, and diminish the disadvantages, of each system might therefore be a promising approach. In this paper, we review hybrid fusion of EMG- and EEG-based control approaches in the bio-robotics field which have been attempted or developed to date. We provide a design overview of the method and consider the main features and merits/disadvantagages for the approaches that have been analyzed. We also discuss the current challenges regarding these hybrid EEG-EMG control approaches and propose some potential future directions.

Keywords: Hybrid EEG-EMG; Bio-Robotics Applications

  • [1] Y. Onishi, S. Oh, Y Hori, New Control Method for Power-Assisted Wheelchair Based on Upper Extermity Movement Using Surface Myoelectric Signal, Proceedings of IEEE 10th International Workshop on Advanced Motion Control, 2008, 498-503Google Scholar

  • [2] T. Felzer, B. Freisleben, HaWCoS: The ”Hands-free” Wheelchair Control System, Proceedings of 5th International ACM SIGCAPH Confernece on Assistive Technologies, 2002, 127-134Google Scholar

  • [3] P. Shenoy, K.J. Miller, B. Crawford, R.P.N. Rao, Electromyographic Control of a Robotic Prosthesis, IEEE Transactions on Biomedical Engineering, 55(2008), 1128-1135CrossrefGoogle Scholar

  • [4] J.L Pons, E. Rocon ,R. Ceres,D. Reynaerts , B. Saro, S. Levin, W.V. Moorleghem, The MANUS-HAND Dextrous Robotics Upper Limb Prosthesis: Mechanical and Manipulation Aspects, Proceedings of International Conferenece on Autonomous Robots, (2004), 143-163CrossrefGoogle Scholar

  • [5] K. Kiguchi, Y. Hayashi, An EMG Based Control for an Upper-Limb Power-Assist Exoskeleton Robot, IEEE Transactions on Systems, Man and Cybernetics-Part B, 42(2012), 1064-1071Google Scholar

  • [6] J. Rosen, M. Brand, B. Moshe, M. Arcan, A Myosignal- Based Powered Exoskeleton System, IEEE Transaction on Systems, Man, and Cybernetics - part a: Systems and Humans, 31(2001), 210-221Google Scholar

  • [7] I. Iturrate, J.M. Antelis,A. Kubler, J. Minguez, A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation, IEEE Transactions on Robotics, 25(2009), 614-627Web of ScienceGoogle Scholar

  • [8] J.del.R. Millan, F. Galan, D. Vanhooydonck, E. Lew, J. Philips, M. Nuttin, Asynchronous Non-Invasive Brain-Activated Control of an Intelligent Wheelchair, Proceedings of Annual International Confernece of The IEEE Engineering in Medicine and Biology Society, (2009), 3361-3364Google Scholar

  • [9] A.R. Murguialday, V. Aggarwal, A. Chatterjee, Y. Cho, R. Rasmussen, B. O’Rourke, S. Acharya, N.V. Thakor, Brain-computer Interface for a Prosthetic Hand Using Local Machine control and Haptic Feedback, Proceedings of IEEE 10th International Conference on Rehabilitation Robotics, (2007), 609-613Google Scholar

  • [10] G.R. Muller-Putz, G. Pfurtscheller, Control of an electrical prosthesis with an SSVEP based BCI, IEEE Transaction on Biomedical Engineering, 55(2008), 361-364Google Scholar

  • [11] Chih-Wei Chen, Chou-Ching K.Lin , Ming-Shaung Ju, Hand Orthosis Controlled Using Brian-Computer Interface, Journal of Medical and Biological Engineeing, 29(2009), 234-241Google Scholar

  • [12] Christine E. King, Po T. Wang, Masato Mizuta, David J. Reinkensmeyer, An H. Do, Shunji Moromugi, Zoran Nenadic, Noninvasive Brain-Computer Interface Driven Hand Orthosis, Proceedings of Annual International Confernece of The IEEE Engineering in Medicine and Biology Society, (2011), 5786-5789Google Scholar

  • [13] Ram Murat Singh , S. Chatterji, Trends and Challenges in EMG based Control Scheme of Exoskeleton Robots- A Review, International Journal of Scienctific and Engineering Research, 3(2012), ISSN 2229-5518Google Scholar

  • [14] Artemiadis. P , EMG-based Robot Control Interfaces: Past, Present and Future, Advances in Robotics & Automation, Editorial Article, 1(2012), DOI:10.4172/ara.1000e107CrossrefGoogle Scholar

  • [15] Guanglin Li , Electromyography Pattern-Recognition-Based Control of Powered Multifunctional Upper-Limb Prostheses, Advances in Applied Electromyography, 1(2011), InTech DOI: 10.5772/22876, http://www.intechopen.com/books/advancesin- applied-electromyography/electromyography-patternrecognition- based-control-of-powered-multifunctional-upperlimb- prosthesesCrossrefGoogle Scholar

  • [16] Jonathan R. Wolpaw, Niels Birbaumer, Dennis J. McFarlanda, Gert Pfurtschellere,Theresa M. Vaughan , Brain-computer interfaces for communication and control, Clinical Neurophysiology, 113(2002), 767-791CrossrefGoogle Scholar

  • [17] F. Lotte, M. Congedo, A. Lecuyer, F. Lamarche , B. Arnaldi , A review of classification algorithms for EEG-based brain-computer interfaces, Journal of Neural Engineering, 4(2007)Google Scholar

  • [18] B. Z. Allison, R. Leeb, C. Brunner, G. R. Muller-Putz, G. Bauernfeind, J. W. Kelly and C. Neuper , Toward smarter BCIs: extending BCIs through hybridization and intelligent control, Journal of Neural Engineering, 9(2012), DOI:10.1088/1741-2560/9/1/013001Web of ScienceCrossrefGoogle Scholar

  • [19] Pfurtscheller G, Allison BZ, Brunner C, Bauernfeind G, Solis- Escalante T, Scherer R, Zander TO, Mueller-Putz G, Neuper C , Birbaumer N , The Hybrid BCI, Frontiers in Neuroscience, (2010), DOI : 10.3389/fnpro.2010.00003CrossrefGoogle Scholar

  • [20] T. Sadoyama, T. Masuda, H. Miyano, Relationship between muscle fiber conduction velocity and frequency parameters of surface EMG during sustained contraction, European Journal of Applied Physiology, 51(1983), 247-256CrossrefGoogle Scholar

  • [21] M. Hagberg, Electromyographic Signs of Shoulder Muscular Fatigue in Two Elevated Arm Positions, Am. J. of Phys. Med., 60(1981), 111-121Google Scholar

  • [22] R. Martini, Aging and the muscular system, Chapter 10: Muscle Tissue, In 5th Edition, Fundamentals of Anatomy and Physiology, Benjamin-Cummings Publishing Company, (2000)Google Scholar

  • [23] P.K. Artemiadis, K.J. Kyriakopoulos, A Switching Regime Model for the EMG-Based Control of a Robot Arm, IEEE Transaction on systems, man, and cybernetics-part B: Cybernetics, 41(2011), 53-63Google Scholar

  • [24] R. Leeb, H. Sagha, R. Chavarriaga and J d R. Millan , A hybrid brain-computer interface based on the fusion of electroencephalographic and electromyographic activities, Journal of Neural Engineering, 8(2011), DOI: 10.1088/1741-2560/8/2/025011CrossrefWeb of ScienceGoogle Scholar

  • [25] R. Leeb, H. Sagha, R. Chavarriaga and J d R. Millan , Multimodal Fusion of Muscle and Brain Signals for a Hybrid-BCI, Proceedings of Annual International Confernece of The IEEE Engineering in Medicine and Biology Society, (2010), 4343-4346Google Scholar

  • [26] E. Rocon, A.F. Ruiz, F. Brunetti, J.L. Pons, J.M. Belda-Lois, J.J. Sanchez-Lacuesta, On the use of an active wearable exoskeleton for tremor suppression via biomechanical loading, Proceedings of IEEE International Conference on Robotics and Automation, (2006), 3140-3145Google Scholar

  • [27] K. Kiguchi, Y. Hayashi, T. Asami, An upper-limb power-assist robot with tremor suppression control, Proceedings of IEEE International Conference on Rehabilitation Robotics, (2011), 1-4Google Scholar

  • [28] E. Rocon, J.A. Gallego, L. Barrios, A.R. Victoria, J. Ibanez, D. Farina, F. Negro, J.L. Dideriksen, S. Conforto T. D Alessio, G. Severini, J.M. Belda-Lois, L.Z. Popovic, G. Grimaldi, M. Manto, J.L. Pons , Multimodal BCI-mediated FES suppression of pathological tremor, Proceedings of Annual International Confernece of The IEEE Engineering in Medicine and Biology Society, (2010), 3337-3340Google Scholar

  • [29] K. Kiguchi, M. Liyanage , A study on a 4DOF Upper-Limb Power-Assist Exoskeleton with Perception-Assist, Proceedings of International Conference on Biomedical Electronics and Devices, (2008), 164-169Google Scholar

  • [30] Kazuo Kiguchi, Manoj Liyanage, Yasunori Kose, Perception Assist with an Active Stereo Camera for an Upper-Limb Power-Assist Exoskeleton, International Journal of Robotics and Mechatronics, 21(2009), 614-620Google Scholar

  • [31] Kazuo Kiguchi, Yoshiaki Hayashi, A Study of EMG and EEG during Perception-Assist with an Upper-Limb Power-Assist Robot, Proceedings of IEEE International Conference on Robotics and Automation, (2012), 2711-2716Google Scholar

  • [32] Subrata Kumar Kundu, Kazuo Kiguchi, Etsuo Horikawa, Design and Control Strategy for a 5 DOF Above-Elbow Prosthetic Arm, International Journal of Assistive Robotics and Mechatronics, 9(2008), 79-93Google Scholar

  • [33] Kazuo Kiguchi, Thilina Dulantha Lalitharatne, Yoshiaki Hayashi, Estimation of Forearm Supination/Pronation Motion Based on EEG Signals to Control an Artificial Arm, Journal of Advanced Mechanical Design, Systems, and Manufacturing, 7(2013), 74-81Web of ScienceGoogle Scholar

  • [34] S.K. Kundu, K. Kiguchi, Development of a 5-DOF Prosthetic Arm for Above Elbow Amputees, Proceedings of IEEE International Conference on Mechatronics and Automation, (2008), 207-212.Google Scholar

  • [35] Yuhuan Du, Xiaodong Zhang, Yang Wang and Tong Mu, Design on Exoskeleton Robot IntelliSense System Based on Multi- Dimensional Information Fusion, Proceedings of IEEE International Conference on Mechatronics and Automation, (2012) , 2435-2439Google Scholar

  • [36] A. Riccio, E. Holtz, P. Arico, F. Leotta, F. Aloise, L. Desideri, A. Rimondini, A. Kubler, D. Mattia, F. Cincotti, Towards a Hybrid Control of a P300-based BCI for Communication in Severely Disabled End-Users, Proceeding of TOBI Workshop IV, Sion, Switzerland, 2013, http://www.tobiproject.org/sites/default/files/public/Publications/TOBI-297.pdfGoogle Scholar

  • [37] G. Cheron, M. Duvinage, C. De Saedeleer, T. Castermans, A. Bengoetxea, M. Petieau, K. Seetharaman, T. Hoellinger, B. Dan, T. Dutoit , F. Sylos Labini, F. Lacquaniti, Y. Ivanenko, From Spinal Central Pattern Generators to Cortical Network: Integrated BCI for Walking Rehabilitation, Neural Plasticity, (2012), DOI : 10.1155/2012/375148Web of ScienceCrossrefGoogle Scholar

  • [38] P. Arico, F. Aloise, F. Pichiorri, F. Leotta, S. Salinari, D. Mattia, F. Cincotti, FES controlled by a hybrid BCI system for neurorehabilitation- driven after stroke, 3th GNB2012, (2012, Rome, Italy), ISBN: 978 88 555 3182-5Google Scholar

  • [39] F. Cincotti, F. Pichiorri, P. Arico, F. Aloise,F. Leotta , F. de Vico Fallani, Jdel R. Millan , M. Molinari, D. Mattia, EEG-based Brain- Computer Interface to support post-stroke motor rehabilitation of the upper limb, Proceedings of Annual International Confernece of The IEEE Engineering in Medicine and Biology Society, (2012), 4112-4115Google Scholar

  • [40] G.R. Muller-Putz, C. Breitwieser, Michael Tangermann, Martijn Schreuder, M. Tavella, R. Leeb, F. Cincotti, F. Leotta, C. Neuper, Tobi hybrid BCI: principle of a new assistive method, International Journal of Bioelectromagnetism,13(2011), 144-145Google Scholar

  • [41] Gernot R. Muller-Putz, Christian Breitwieser, Febo Cincotti, Robert Leeb, Martijn Schreuder, Francesco Leotta, Michele Tavella, Luigi Bianchi , Alex Kreilinger, Andrew Ramsay, Martin Rohm, Max Sagebaum, Luca Tonin, Christa Neuper, Josedel. R. Millan, Tools for brain-computer interaction: a genaral concept for a hybrid BCI, Frontiers in Neuroinformatics,(2011), DOI: 10.3389/fninf. 2011.00030CrossrefGoogle Scholar

  • [42] Jun Yao and Julius P. A. Dewald, Cortico-muscular communication during the genaration of static shoulder abduction torque in upper limb following stoke, Proceedings of Annual International Confernece of The IEEE Engineering in Medicine and Biology Society, (2006), 181-184Google Scholar

  • [43] Qi Yang, Vlodek Siemionow, Wanxiang Yao, Vinod Sahgal, Guang H. Yue, Single-Trial EEG-EMG Coherence Analysis Reveals Muscle Fatigue-Related Progressive Alterations in Corticomuscular Coupling, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(2010), 97-106Google Scholar

  • [44] J. R. Wolpaw, D. J. McFarland, and T. M. Vaughan, Brain- Computer Interface Research at the Wadsworth Center, IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, (2000), 222-226Google Scholar

  • [45] Theresa M. Vaughan, Jonathan R. Wolpaw, and Emanuel Donchin, EEG-Based Communication: Prospects and Problems, IEEE Transactions on Rehabilitation Engineering, vol. 4, no.4, (1996), 425-430 Google Scholar

About the article

Published Online: 2013-12-10

Published in Print: 2013-12-01


Citation Information: Paladyn, Journal of Behavioral Robotics, ISSN (Print) 2081-4836, DOI: https://doi.org/10.2478/pjbr-2013-0009.

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