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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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References

  1. Mak JN, Wolpaw JR (2009) Clinical applications of brain-computer interfaces: current state and future prospects. IEEE Rev Biomed Eng 2:187–199

    Google Scholar 

  2. Anupama HS, Cauvery NK, Lingaraju GM (2012) Brain computer interface and its types-a study. Int J Adv Eng Technol 3:739–745

    Google Scholar 

  3. Shih JJ, Krusienski DJ, Wolpaw JR (2012) Brain-computer interfaces in medicine. Mayo Clin Proc 87(3):268–279

    Google Scholar 

  4. Niedermeyer E, da Silva FL (2004) Electroencephalography: basic principles, clinical applications, and related fields. Lippincott, Lippincott Williams and Wilkins

    Google Scholar 

  5. Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993) Magnetoencephalography theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65(2):413–497. https://doi.org/10.1103/RevModPhys.65.413

    Article  Google Scholar 

  6. Huettel SA, Song AW, McCarthy G (2009) Functional magnetic resonance imaging, 2nd edn. Sinauer, Massachusetts

    Google Scholar 

  7. Coyle SM, Ward TSE, Markham CM (2007) Brain–computer interface using a simplified functional near-infrared spectroscopy system. J Neural Eng 4(3):219–226

    Google Scholar 

  8. Anderson RA, Musallam S, Pesaran B (2004) Selecting the signals for a brain-machine interface. Curr Opin Neurobiol 14(6):720–726

    Google Scholar 

  9. Nicolas-Alonso LF, Gomez-Gil J (2012) Brain computer interfaces, a review. Sensors 12:2

    Google Scholar 

  10. Bi L, Fan XA, Liu Y (2013) EEG-based brain-controlled mobile robots: a survey. IEEE Trans Human-Mach Syst 43(2):161–176

    Google Scholar 

  11. Krishnan NM, Mariappan M, Muthukaruppan K, Hijazi M, Kitt W (2016) Electroencephalography (EEG) based control in assistive mobile robots: a review. IOP Conf Ser Mater Sci Eng 121:150

    Google Scholar 

  12. Rechy-Ramirez EJ, Hu H (2015) Bio-signal based control in assistive robots: a survey. Digital Commun Netw 27:999

    Google Scholar 

  13. Pfurtscheller G, da Silva FL (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110(11):1842–1857

    Google Scholar 

  14. Wolpaw JR, McFarland DJ (2004) Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci 101:17849–17854

    Google Scholar 

  15. Pfurtscheller G, Neuper C (2001) Motor imagery and direct brain-computer communication. Proc IEEE 89:1123–1134

    Google Scholar 

  16. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113:767–791

    Google Scholar 

  17. Serby H, Yom-Tov E, Inbar GF (2005) An improved P300-based brain–computer interface. IEEE Trans Neural Syst Rehabil Eng 13:89–98

    Google Scholar 

  18. Middendorf M, McMillan G, Calhoun G, Jones KS (2000) Brain–computer interfaces based on the steady-state visual-evoked response. IEEE Trans Rehabil Eng 8:211–214

    Google Scholar 

  19. Wang Y, Wang R, Gao X, Hong B, Gao S (2006) A practical VEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 14:234–239. https://doi.org/10.1109/tnsre.2006.875576

    Article  Google Scholar 

  20. Ding J, Sperling G, Srinivasan R (2006) Attentional modulation of SSVEP power depends on the network tagged by the flicker frequency. Cereb Cortex 16(7):1016–1029

    Google Scholar 

  21. Jasper HH (1958) The ten twenty electrode system of the international federation. Electroencephalogr Clin Neurophysiol 10:371–375

    Google Scholar 

  22. Herrmann CS (2001) Human EEG responses to 1-100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena. Exp Brain Res 137(3–4):346–353

    Google Scholar 

  23. Wu Z, Lai Y, Xia Y, Wu D, Yao D (2008) Stimulator selection in SSVEP-based BCI. Med Eng Phys 30(8):1079–1088

    Google Scholar 

  24. Ergen M, Marbach S, Brand A, Ar-Eroglu BC, Demiralp T (2008) P3 and delta band responses in visual oddball paradigm in schizophrenia. Neurosci Lett 440(3):304–308

    Google Scholar 

  25. Dasgupta S, Fanton M, Pham J, Willard M, Nezamfar H, Shafai B, Erdogmus D (2010) Brain controlled robotic platform using steady state visual evoked potentials acquired by EEG. In: Proceedings of conference record forty fourth Asilomar conference signals, systems computers, pp 1371–1374

  26. Prueckl R, Guger C (2010) Controlling a robot with a brain–computer interface based on steady state visual evoked potentials. In: Proceedings of international joint conference neural network, pp 1–5

  27. Ortner R, Guger C, Prueckl R, Grünbacher E, Edlinger G (2010) SSVEP-based brain-computer interface for robot control. In: Proceedings 12th international conference computers helping people with special needs, Vienna, Austria, pp 85–90

  28. Pires G, Castelo-Branco M, Nunes U (2008) Visual P300-based BCI to steer a wheelchair: a Bayesian approach. In: Proceedings of IEEE engineering in medicine and biology society, British Columbia, Canada, pp 658–661

  29. Choi K, Cichocki A (2008) Control of a wheelchair by motor imagery in real time. In: Proceedings of 9th international conference intelligence data engineering automation learning, pp 330–337

  30. Leeb R, Friedman D, Muller-Putz G, Scherer R, Slater M, Pfurtscheller G (2007) Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput Intell Neurosci 2007:1–7

    Google Scholar 

  31. Craig D, Nguyen H (2007) Adaptive EEG thought pattern classifier for advanced wheelchair control. Proc 29th Annu Int Conf IEEE Eng Med Biol Soc 75:978–989

    Google Scholar 

  32. Chae Y, Jo S, Jeong J (2011) Brain-actuated humanoid robot navigation control using asynchronous brain-computer interface. In Proceedings of 5th international IEEE/EMBS conference neural engineering, Cancun, Mexico, pp 519–524

  33. Choi K (2011) Control of a vehicle with EEG signals in real-time and system evaluation. Eur J Appl Physiol 112(2):755–766

    Google Scholar 

  34. Birbaumer N, Elbert T, Canavan AG, Rockstroh B (1990) Slow potentials of the cerebral cortex and behavior. Physiol Rev 1990(70):1–41

    Google Scholar 

  35. Millán JR, Renkens F, Mouriño J, Gerstner W (2004) Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans Bio Eng 51(6):1026–1033

    Google Scholar 

  36. Tanaka K, Matsunaga K, Wang HO (2005) Electroencephalogram based control of an electric wheelchair. IEEE Trans Robot 21(4):762–766

    Google Scholar 

  37. Barbosa AOG, Achanccaray DR, Meggiolaro MA (2010) Activation of a mobile robot through a brain computer interface. In: Proceedings of conference rectifier IEEE international conference robotic automation Anchorage convention district, Anchorage, AK, pp 4815–4821

  38. Vanacker G, Millán JR, Lew E, Ferrez PW, Moles FG, Philips J, Brussel HV, Nuttin M (2007) Context-based filtering for brain-actuated wheelchair driving. Comput Intell Neurosci 2007:1–12

    Google Scholar 

  39. Ron-Angevin R, Velasco-Alvarez F, Sancha-Ros S, da Silva-Sauer L (2011) A two-class self-paced BCI to control a robot in four directions. In: Proceedings of IEEE international conference rehabilitation robotics, pp 1–6

  40. Velasco-Álvarez F, Ron-Angevin R, da Silva-Sauer L, Sancha-Ros S, Blanca-Mena MJ (2011) Audio-cued SMR brain–computer interface to drive a virtual wheelchair. In: Proceedings of 11th international conference artificial neural networks conference advanced computing intelligence, pp 337–344

  41. Gomez-Rodriguez M, Grosse-Wentrup M, Hill J, Gharabaghi A, Schölkopf B, Peters J (2011) Towards brain–robot interfaces in stroke rehabilitation. In: Proceedings IEEE international conference rehabilitation robotics, pp 1–6

  42. Galan F, Nuttin M, Lew E, Ferrez PW, Vanacker G, Philips J, Millán JR (2008) A brain-actuated wheelchair: asynchronous and noninvasive brain–computer interfaces for continuous control of robots. Clin Neurophysiol 119(9):2159–2169

    Google Scholar 

  43. Philips J, Millán JR, Vanacker G, Lew E, Galan F, Ferrez PW, Brussel HV, Nuttin M (2007) Adaptive shared control of a brain-actuated simulated wheelchair. In: Proceedings conference rectifier IEEE 10th international conference rehabilitation robotics, Noordwijk, The Netherlands, pp 408–414

  44. Millán JR, Galan F, Vanhooydonck D, Lew E, Philips J, Nuttin M (2009) Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. In: Proceedings conference rectifier 2009 IEEE/EMBS 31st annual international conference, Minneapolis, MN, pp 3361–3364

  45. Satti AR, Coyle D, Prasad G (2011) Self-paced brain-controlled wheelchair methodology with shared and automated assistive control. In: Proceedings conference rectifier 2011 IEEE symposium series on computational intelligence, Paris, France, pp 120–127

  46. Hema CR, Paulraj MP, Yaacob S, Hamid AH, Nagarajan R (2009). Mu and beta EEG rhythm based design of a four state brain machine interface for a wheelchair. In: Proceedings of international conference control, automation, communication energy conservation, Tamil Nadu, India, pp 401–404

  47. Geng T, Dyson M, Tsui CS, Gan JQ (2007) A 3-class asynchronous BCI controlling a simulated mobile robot. In: Proceedings conference rectifier 2007IEEE/EMBS 29th annual international conference, Lyon, France, pp 2524–2527

  48. Tsui CSL, Gan JQ, Roberts SJ (2009) A self-paced brain–computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training. Med Biol Eng Comput 47(3):257–265

    Google Scholar 

  49. Tsui CSL, Gan JQ (2007) Asynchronous BCI control of a robot simulator with supervised online training. In: Proceedings 8th international conference intelligence data engineering automated learning, pp 125–134

  50. Geng T, Gan JQ (2008) Motor prediction in brain–computer interfaces for controlling mobile robots. In: Proceedings conference rectifier 2008 IEEE/EMBS30th annual international conference, Vancouver, BC, Canada, pp 634–637

  51. Hema CR, Paulraj MP (2011) Control brain machine interface for a power wheelchair. In: Proceedings of 5th Kuala Lumpur international conference biomedical engineering, pp 287–291

  52. Fan TL, Ng CS, Ng JQ, Goh SY (2008) A brain–computer interface with intelligent distributed controller for wheelchair. In: Proceedings of IFMBE, pp 641–644

  53. Hema CR, Paulraj MP, Yaacob S, Adom AH, Nagarajan R (2011) Software tools algorithms for biological systems. Adv Exp Med Biol 57:99

    Google Scholar 

  54. Hema CR, Paulraj MP, Yaacob S, Adom AH, Nagarajan R (2010) Robot chair control using an asynchronous brain machine interface. In: Proceedings of 6th international colloquium signal process, pp 21–24

  55. Ferreira A, Bastos-Filho TF, Sarcinelli-Filho M, Sánchez JLM, García JCG, Quintas MM (2010) Improvements of a brain–computer interface applied to a robotic wheelchair. Biomed Eng Syst Technol 52:64–73

    Google Scholar 

  56. Lee K, Liu D, Perroud L, Chavarriaga R, Millán JR (2016) A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers. Robot Auton Syst 56:988

    Google Scholar 

  57. Batula AM, Mark J, Kim YE et al. (2016) Developing an optical brain-computer interface for humanoid robot control. In: Proceedings of the 10th international conference on foundations of augmented cognition: neuroergonomics and operational neuroscience

  58. Aljalal M, Djemal R, Ibrahim S (2018) Robot navigation using a brain computer interface based on motor imagery. J Med Biol Eng. https://doi.org/10.1007/s40846-018-0431-9

    Article  Google Scholar 

  59. Varona-Moya S, Velasco-Álvarez F, Sancha-Ros S, Fernández-Rodríguez Á, Blanca MJ, Ron-Angevin R (2015) Wheelchair navigation with an audio-cued, two-class motor imagery-based brain-computer interface system. In: 2015 7th international IEEE/EMBS conference on neural engineering (NER), Montpellier, pp 174–177

  60. Ron-Angevin R, Velasco-Álvarez F, Fernández-Rodríguez Á, Díaz-Estrella A, Blanca-Mena MJ, Vizcaíno-Martín FJ (2017) Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair. J NeuroEng Rehabil 56:125

    Google Scholar 

  61. Yu Y, Zhou Z, Yin E, Jiang J, Tang J, Liu Y, Hu D (2016) Toward brain-actuated car applications: self-paced control with a motor imagery-based brain-computer interface. Comput Biol Med 77:148–155

    Google Scholar 

  62. Mandel C, Luth T, Laue T, Rofer T, Graser A, Krieg-Bruckner B (2009). Navigating a smart wheelchair with a brain–computer interface interpreting steady-state visual evoked potentials. In: Proceedings conference rectifier 2009IEEE/RSJ international conference intelligence robots system, St. Louis, MO, pp 1118–1125

  63. Prueckl R, Guger C (2009) A brain–computer interface based on steady state visual evoked potentials for controlling a robot. In: Proceedings of 10th international work-conference artificial neural network: part i: bio-inspired system: computing ambient intelligence, pp 690–697

  64. Lee P-L, Chang H-C, Hsieh T-Y, Deng H-T, Sun C-W (2012) A brain-wave-actuated small robot car using ensemble empirical mode decomposition-based approach. IEEE Trans Syst Man Cybern A Syst Humans 40(5):1053–1064

    Google Scholar 

  65. Hu H, Zhao J, Li H, Li W, Chen G (2016) Telepresence control of humanoid robot via high-frequency phase-tagged SSVEP stimuli. In: 2016 IEEE 14th international workshop on advanced motion control (AMC), Auckland, pp 214–219

  66. Legény J, Abad RV, Lécuyer A (2011) Navigating in virtual worlds using a self-paced SSVEP-based brain-computer interface with integrated stimulation and real-time feedback. Presence 20(6):529–544

    Google Scholar 

  67. Bryan M, Green J, Chung M, Chang L, Scherer R, Smith J, Rao RPN (2011) An adaptive brain-computer interface for humanoid robot control. In: 2011 11th IEEE-RAS international conference on humanoid robots, Bled, pp 199–204

  68. Zhao J, Li W, Mao X, Li M (2015) SSVEP-based experimental procedure for brain-robot interaction with humanoid robots. J Vis Exp 105:e53558. https://doi.org/10.3791/53558

    Article  Google Scholar 

  69. Zhao J, Li W, Li M (2015) Comparative study of SSVEP- and P300-based models for the telepresence control of humanoid robots. PLoS ONE 10(11):e0142168

    Google Scholar 

  70. Li M, Li W, Zhao J et al (2014) A P300 model for Cerebot—a mind controlled humanoid robot. Robot Intell Technol Appl 2:495–502

    Google Scholar 

  71. Güneysu A, Akin LH (2013) An SSVEP based BCI to control a humanoid robot by using portable EEG device. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc. https://doi.org/10.1109/EMBC.2013.6611145

    Article  Google Scholar 

  72. Zhao J, Meng Q, Li W, Li M, Chen G (2014) SSVEP-based hierarchical architecture for control of a humanoid robot with mind. In: Proceedings of the 11th world congress on intelligent control and automation (WCICA ’14), pp 2401–2406

  73. Deng Z, Li X, Zheng K, Yao W (2011) A humanoid robot control system with SSVEP-based asynchronous brain-computer interface. Jiqiren/Robot 33(2):129–135

    Google Scholar 

  74. Achic F, Montero J, Penaloza C, Cuellar F (2016) Hybrid BCI system to operate an electric wheelchair and a robotic arm for navigation and manipulation tasks. In: 2016 IEEE workshop on advanced robotics and its social impacts (ARSO), Shanghai, pp 249–254

  75. Chen X, Zhao B, Wang Y, Xu S, Gao X (2018) Control of a 7-DOF robotic arm system with an SSVEP-based BCI. Int J Neural Syst 28(8):1850018

    Google Scholar 

  76. Shao L, Zhang L, Belkacem AN, Zhang Y, Chen X, Li J, Liu H (2020) EEG-controlled wall-crawling cleaning robot using SSVEP-based brain-computer interface. J Healthcare Eng 76:129

    Google Scholar 

  77. Iturrate I, Antelis JM, Kubler A, Minguez J (2009) A noninvasive brain-actuated wheelchair based on a p300 neurophysiological protocol and automated navigation. IEEE Trans Robot 25(3):614–627

    Google Scholar 

  78. Escolano C, Antelis J, Minguez J (2009) Human brain-teleoperated robot between remote places. Proc. IEEE Int Conf Robot Autom, Kobe, pp 4430–4437

    Google Scholar 

  79. Escolano C, Murguialday AR, Matuz T, Birbaumer N, Minguez J (2011) A telepresence robotic system operated with a P300-basedbbrain–computer interface initial tests with ALS patients. In: Proceedings of conference rectifier 2011 IEEE/EMBS 32nd annual international conference, Buenos Aires, Argentina, pp 4476–4480

  80. Iturrate I, Antelis J, Minguez J (2009) Synchronous EEG brain-actuated wheelchair with automated navigation. In: Proceedings of conference rectifier 2009 IEEE international conference robotics automation, pp 2530–2537

  81. Tang J, Jiang J, Yu Y, Zhou Z (2015) Humanoid robot operation by a brain-computer interface. In: Proceedings of the 7th international conference on information technology in medicine and education (ITME ’15), pp 476–479

  82. Khalid MB, Rao NI, Rizwan-i-Haque I, Munir S, Tahir F (2009). Towards a brain computer interface using wavelet transform with averaged and time segmented adapted wavelets. In: 2009 2nd international conference on computer, control and communication, pp 1–4

  83. http://www.bci2000.org/wiki/index.php/User_Tutorial:EEG_Measurement_Setup

  84. Chae Y, Jeong J, Jo S (2012) Toward brain-actuated humanoid robots: asynchronous direct control using an EEG-based BCI. IEEE Trans Robot 28:1–14

    Google Scholar 

  85. Alshbatat A, Shubeilat F (2013) Brain-computer interface for controlling a mobile robot utilizing an electroencephalogram signals. In: 4th world conference on information technology

  86. Jiménez Moreno R, Rodríguez Alemán J (2015) Control of a mobile robot through brain computer interface. INGE CUC 11(2):74–83

    Google Scholar 

  87. Yordanov Y, Tsenov G, Mladenov V (2017) Humanoid robot control with EEG brainwaves. In: 2017 9th IEEE international conference on intelligent data acquisition and advanced computing systems: technology and applications (IDAACS), Bucharest, pp 238–242

  88. Jenita ARB, Umamakeswari A (2015) Electroencephalogram-based brain controlled robotic wheelchair. Indian J Sci Technol 8:188–197

    Google Scholar 

  89. Rajesh KV, Joseph KO (2016) Brain controlled mobile robot using brain wave sensor. IOSR J VLSI Signal Process 2319:77–82

    Google Scholar 

  90. Tsui CS, Gan JQ, Hu H (2011) A self-paced motor imagery based brain-computer interface for robotic wheelchair control. Clin EEG Neurosci 42(4):225–229

    Google Scholar 

  91. Huang D, Qian K, Fei Y, Jia W, Chen X, Bai O (2012) Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control. IEEE Trans Neural Syst Rehabil Eng 20(3):379–388

    Google Scholar 

  92. Kilicarslan A, Prasad S, Grossman RG, Contreras-Vidal JL (2013) High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Osaka, pp 5606–5609

  93. Song W, Wang X, Zheng S, Lin Y (2014) Mobile robot control by BCI based on motor imagery. Intelligent human-machine systems and cybernetics (IHMSC). In: 2014 sixth international conference on intelligent human-machine systems and cybernetics, Hangzhou, pp 383–387

  94. Gao Q, Dou L, Belkacem AN, Chen C (2017) Noninvasive electroencephalogram based control of a robotic arm for writing task using hybrid BCI system. BioMed Res Int 2017:8

    Google Scholar 

  95. Müller-Putz GR, Pfurtscheller G (2008) Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Trans Biomed Eng 55(1):361–364

    Google Scholar 

  96. Pfurtscheller G, Solis-Escalante T, Ortner R, Linortner P, Muller-Putz GR (2010) Self-paced operation of an SSVEP-based orthosis with and without an imagery-based “brain switch:” a feasibility study towards a hybrid BCI. IEEE Trans Neural Syst Rehabil Eng 18(4):409–414

    Google Scholar 

  97. Ortner R, Allison BZ, Korisek G, Gaggl H, Pfurtscheller G (2011) An SSVEP BCI to control a hand orthosis for persons with tetraplegia. IEEE Trans Neural Syst Rehabil Eng 19(1):1–5

    Google Scholar 

  98. Horki P, Solis-Escalante T, Neuper C, Müller-Putz G (2011) Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb. Med Biol Eng Compu 49(5):567–577

    Google Scholar 

  99. Choi B, Jo S (2013) A low-cost EEG system-based hybrid brain computer interface for humanoid robot navigation and recognition. PLoS ONE 8(9):e74583

    Google Scholar 

  100. Chu Y, Zhao X, Han J, Zhao Y, Yao J (2014) SSVEP based brain-computer interface controlled functional electrical stimulation system for upper extremity rehabilitation. In: 2014 IEEE international conference on robotics and biomimetics (ROBIO 2014), Bali, pp 2244–2249

  101. Escolano C, Antelis JM, Minguez J (2012) A telepresence mobile robot controlled with a noninvasive brain–computer interface. IEEE Trans Syst Man Cybern B Cybern 42(3):793–804

    Google Scholar 

  102. Panicker RC, Puthusserypady S, Sun Y (2011) An asynchronous P300 BCI with SSVEP-based control state detection. IEEE Trans Biomed Eng 58(6):1781–1788

    Google Scholar 

  103. Li M, Li W, Zhou H (2016) Increasing N200 potentials via visual stimulus depicting humanoid robot behavior. Int J Neural Syst 26(1):1550039

    Google Scholar 

  104. Zhang Z, Huang Y, Chen S, Qu J, Pan X, Yu T, Li Y (2017) An intention-driven semi-autonomous intelligent robotic system for drinking. Front Neurorobot 11:48. https://doi.org/10.3389/fnbot.2017.00048

    Article  Google Scholar 

  105. Wang F, Zhang X, Fu R, Sun G (2018) Study of the home-auxiliary robot based on BCI. Sensors 18:1779. https://doi.org/10.3390/s18061779

    Article  Google Scholar 

  106. Spüler M (2017) Spatial filtering of EEG as a regression problem. Sensor. https://doi.org/10.3217/978-3-85125-533-1-84

    Article  Google Scholar 

  107. Mallick A, Kapgate D (2015) A review on signal pre-processing techniques in brain computer interface. Int J Comput Technol 2(4):130–134

    Google Scholar 

  108. Shedeed HA, Issa MF, El-sayed SM (2013) Brain EEG signal processing for controlling a robotic arm. In: 2013 8th international conference on computer engineering and systems (ICCES), Cairo, pp 152–157

  109. Chae Y, Jeong J, Jo S (2011) Noninvasive brain-computer interface-based control of humanoid navigation. In: 2011 IEEE/RSJ international conference on intelligent robots and systems, San Francisco, CA, pp 685–691

  110. Bhattacharyya S, Sengupta A, Chakraborti T, Banerjee D, Khasnobish A, Konar A, Tibarewala DN, Janarthanan R (2012). EEG controlled remote robotic system from motor imagery classification. In: 2012 third international conference on computing communication and networking technologies (ICCCNT), Coimbatore, pp 1–8

  111. Cho S-Y, Winod AP, Cheng KWE (2009) Towards a Brain-Computer Interface based control for next generation electric wheelchairs. In: 2009 3rd international conference on power electronics systems and applications (PESA), Hong Kong, pp 1–5

  112. Bento VA, Cunha JP, Silva FM (2008) Towards a human-robot interface based on the electrical activity of the brain. In: Humanoids 2008—8th IEEE-RAS international conference on humanoid robots, Daejeon, pp 85–90

  113. Fan XA, Bi L, Teng T, Ding H, Liu Y (2015) A brain-computer interface-based vehicle destination selection system using P300 and SSVEP signals. IEEE Trans Intell Transp Syst 16(1):274–283

    Google Scholar 

  114. Muller K-R, Anderson CW, Birch GE (2003) Linear and nonlinear methods for brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 11(2):165–169

    Google Scholar 

  115. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, England

    MATH  Google Scholar 

  116. Baudat G, Anouar F (2000) Generalized discriminant analysis using a kernel approach. Neural Comput 12(10):2385–2404

    Google Scholar 

  117. Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167

    Google Scholar 

  118. Ouyang W, Cashion K, Asari VK (2013) Electroencephalograph based brain machine interface for controlling a robotic arm. In: 2013 IEEE applied imagery pattern recognition workshop (AIPR), Washington, DC, pp 1–7

  119. Li T, Hong J, Zhang J, Guo F (2014) Brain–machine interface control of a manipulator using small-world neural network and shared control strategy. J Neurosci Methods 224:26–38

    Google Scholar 

  120. Bhattacharyya S, Basu D, Konar A, Tibarewala DN (2015) Interval type-2 fuzzy logic based multiclass ANFIS algorithm for real-time EEG based movement control of a robot arm. Robot Auto Syst 68:104–115

    Google Scholar 

  121. Denoeux T (1995) A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans Syst Man Cyber 25:804–813

    Google Scholar 

  122. Borisoff JF, Mason SG, Bashashati A, Birch GE (2004) Brain-computer interface design for asynchronous control applications: Improvements to the LF-ASD asynchronous brain switch. IEEE Trans Biomed Eng 2004(51):985–992

    Google Scholar 

  123. Rebsamen B, Guan C, Zhang H, Wang C, Teo C, Ang MH Jr, Burdet E (2010) A brain controlled wheelchair to navigate in familiar environments. IEEE Trans Neural Syst Rehabil Eng 18(6):590–598

    Google Scholar 

  124. Rebsamen B, Burdet E, Guan C, Teo CL, Zeng Q, Ang M, Laugier C (2007) Controlling a wheelchair using a BCI with low information transfer rate. In: Proceedings of conference rectifier 2007 IEEE 10th international conference rehabilitation robot, Noordwijk, Netherlands, pp 1003–1008

  125. Rebsamen B, Teo CL, Zeng Q, Ang MH Jr, Burdet E, Guan C, Zhang HH, Laugier C (2007) Controlling a wheelchair indoors using thought. IEEE Intell Syst 22(2):18–24

    Google Scholar 

  126. Rebsamen B, Burdet E, Guan C, Zhang H, Teo CL, Zeng Q, Ang M, Laugier C (2006) A brain controlled wheelchair based on P300 and path guidance. In: Proceedings of IEEE/RAS-EMBS international conference biomedical robotics biomechatronics, pp 1001–1006

  127. Geng T, Gan JQ, Hu H (2010) A self-paced online BCI for mobile robot control. Int J Adv Mechantron Syst 2:28–35

    Google Scholar 

  128. Bougrain L, Duvinage M, Klein E (2012) Inverse reinforcement learning to control a robotic arm using a Brain-Computer Interface. In: Research Report

  129. Úbeda A, Iáñez E, Azorín JM (2013) Shared control architecture based on RFID to control a robot arm using a spontaneous brain–machine interface. Robot Auto Syst 61(8):768–774

    Google Scholar 

  130. Cantillo-Negrete J, Carino-Escobar RI, Carrillo-Mora P, Elias-Vinas D, Gutierrez-Martinez J (2018) Motor imagery-based brain-computer interface coupled to a robotic hand orthosis aimed for neurorehabilitation of stroke patients. J Healthcare Eng. https://doi.org/10.1155/2018/1624637

    Article  Google Scholar 

  131. Jianjun M, Shuying Z, Angeliki B, Jaron O, Bryan B, Bin H (2016) Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks. In: Scientific Reports, VL 6, Article number: 38565

  132. Tonin L, Carlson T, Leeb R, Millán JR (2011) Brain-controlled telepresence robot by motor-disabled people. In: Proceedings of 33rd annual international conference IEEE engineering medical biology society, Boston, MA, pp 4227–4230

  133. Hortal E, Úbeda A, Iáñez E, Azorín JM (2014) Control of a 2 DoF robot using a brain-machine interface. Comput Methods Progr Biomed 116:169–176

    Google Scholar 

  134. Bhattacharyya S, Konar A, Tibarewala DN (2014) Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose. Med Biol Eng Comput 52:1007–1017

    Google Scholar 

  135. Hortal E, Planelles D, Costa A, Iáñez E, Úbeda A, Azorín JM, Fernández E (2015) SVM-based Brain-Machine Interface for controlling a robot arm through four mental tasks. Neurocomputing 151(1):116–121

    Google Scholar 

  136. Roy R, Mahadevappa M, Cheruvu K (2016) Trajectory path planning of EEG controlled robotic arm using GA. Procedia Comput Sci 84:147–151. https://doi.org/10.1016/j.procs.2016.04.080

    Article  Google Scholar 

  137. Zhang H, Guan C, Wang C (2008) Asynchronous P300-based brain–computer interfaces: a computational approach with statistical models. IEEE Trans Biomed Eng 55(6):1754–1763

    Google Scholar 

  138. Lopes AC, Pires G, Vaz L, Nunes U (2011). Wheelchair navigation assisted by human-machine shared-control and a P300-based brain computer interface. In: Proceedings of conference rectifier 2011 IEEE/RSJ international conference intelligence robots systems, San Francisco, CA, pp 2438–2444

  139. Shi BG, Kim T, Jo S (2010) Non-invasive brain signal interface for a wheelchair navigation. In: Proceedings 2010 international conference control automation systems, Gyeonggi-do, Korea, pp 2257–2260

  140. Bell CJ, Shenoy P, Chalodhorn R, Rao RPN (2008) Control of a humanoid robot by a noninvasive brain–computer interface in humans. J Neural Eng 5(2):214–220

    Google Scholar 

  141. Tang J, Zhou Z, Liu Y (2017) A 3D visual stimuli based P300 brain-computer interface: for a robotic. Arm Control. https://doi.org/10.1145/3080845.3080863

    Article  Google Scholar 

  142. http://www.neurotechcenter.org/research/bci2000/dissemination

  143. Schalk G, McFarland DJ, Hinterberger T, Birbaumer V, Wolpaw JR (2004) BCI2000: A general-purpose brain–computer interface (BCI) system. IEEE Trabs Biomed Eng 51(6):1034–1043

    Google Scholar 

  144. http://openvibe.inria.fr

  145. Renard Y, Lotte F, Gibert G, Congedo M, Maby E, Delannoy V, Bertrand O, Lécuyer A (2010) OpenViBE: an open-source software platform to design, test and use brain-computer interfaces in real and virtual environments, presence: teleoperators and virtual environments. Sensor 19(1):2010

    Google Scholar 

  146. Delorme A, Makeig S (2004) EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics. J Neurosci Methods 134:9–21

    Google Scholar 

  147. Yendrapalli K, Tammana SS (2014) The brain signal detection for controlling the robot. Int J Sci Eng Technol 3(10):1280–1283

    Google Scholar 

  148. Anupama HS, Cauvery NK, Lingaraju GM (2014) Brain controlled wheelchair for disabled. Int J Comput Sci Eng Inf Technol Res 4(2):157–166

    Google Scholar 

  149. Torres Müller SM, Bastos-Filho TF, Sarcinelli-Filho M (2011) Using a SSVEP-BCI to command a robotic wheelchair. In: Proceedings of conference rectifier 2011 IEEE international symposium industrial electronic, pp 957–962

  150. Chung M, Cheung W, Scherer R, Rao RPN (2011) Towards hierarchical BCls for robotic control. In: Proceedings of conference rectifier 2011 IEEE/EMBS 5th international conference neural engineering, Cancun, Mexico, pp 330–333

  151. Jiang J, Wang A, Ge Y, Zhou Z (2013) Brain-actuated humanoid robot control using one class motor imagery task. In: Proceedings of the Chinese automation congress (CAC ’13), pp 587–590

  152. Perrin X, Chavarriaga R, Colas F, Siegwart R, Millán JR (2010) Brain-coupled interaction for semi-autonomous navigation of an assistive robot. Robot Autonom Syst 58(12):1246–1255

    Google Scholar 

  153. Gergondet P, Druon S, Kheddar A, Hintermuller C, Guger C, Slater M (2011) Using brain-computer interface to steer a humanoid robot. In: Proceedings of the IEEE international conference on robotics and biomimetics (ROBIO ’11), pp 192–197

  154. Wang X-Y, Cai F, Jin J, Zhang Y, Wang B (2015) Robot control system based on auditory brain-computer interface. Control Theory Appl 32(9):1183–1190

    Google Scholar 

  155. Liu J-C, Chou H-C, Chen C-H, Lin Y-T, Kuo C-H (2016) Time-shift correlation algorithm for P300 event related potential brain-computer interface implementation. Comput Intell Neurosci 2016:3039454

    Google Scholar 

  156. Pfurtscheller G, Allison BZ, Brunner C, Bauernfeind G, Solis- Escalante T, Scherer R, Zander TO, Mueller-Putz G, Neuper C, Birbaumer N (2010) The hybrid BCI. Front Neurosci 4(42):1–11

    Google Scholar 

  157. Leeb R, Sagha H, Chavarriaga R, Millán JR (2010) Multimodal fusion of muscle and brain signals for a hybrid-BCI. In: Proceedings of conference rectifier 2010 IEEE 32nd annual international conference engineering medical biology Society, Buenos Aires, Argentina, pp 4343–4346

  158. Long J, Li Y, Yu T, Gu Z (2012) Target selection with hybrid feature for BCI-based 2-D cursor control. IEEE Trans Biomed Eng 59(1):132–140

    Google Scholar 

  159. Leeb R, Tonin L, Rohm M, Desideri L, Carlson T, Millán JDR (2015) Towards independence: a BCI telepresence robot for people with severe motor disabilities. Proc IEEE 103(6):969–982

    Google Scholar 

  160. Carlson T, Millán JR (2013) Brain-controlled wheelchairs: a robotic architecture. IEEE Robot Automat Mag 20(1):65–73

    Google Scholar 

  161. Zhang R, Li Y, Yan Y, Zhang H, Wu S, Yu T, Gu Z (2016) Control of a wheelchair in an indoor environment based on a brain-computer interface and automated navigation. IEEE Trans Neural Syst Rehabil Eng 24(2016):128–139

    Google Scholar 

  162. Carlson T, Demiris Y (2008) Human–wheelchair collaboration through prediction of intention and adaptive assistance. In: Proceedings of IEEE international conference robotics automations, Pasadena, CA, pp 3296–3931

  163. Demeester E, Nuttin M, Vanhooydonck D, Brussel HV (2003) Assessing the user’s intent using bayes’ rule: application to wheelchair control. In: Proceedings of 1st international workshop advances in service robotics

  164. Tonin L, Leeb R, Tavella M, Perdikis S, Millán JR (2010) The role of shared-control in BCI-based telepresence. In: Proceedings of IEEE international conference systems man cybernetics, pp 1462–1466

  165. Puanhvuan D, Khemmachotikun S, Wechakarn P, Wijarn B, Wongsawat Y (2017) Navigation-synchronized multimodal control wheelchair from brain to alternative assistive technologies for persons with severe disabilities. Cogn Neurodyn 11(2017):117–134

    Google Scholar 

  166. Cui X, Bray S, Bryant DM, Glover GH, Reiss AL (2011) A quantitative comparison of NIRS and fMRI across multiple cognitive tasks. NeuroImage 54(4):2808–2821

    Google Scholar 

  167. Batula AM, Kim YE, Ayaz H (2017) Virtual and actual humanoid robot control with four-class motor-imagery-based optical brain-computer interface. Biomed Res Int 2017:1463512. https://doi.org/10.1155/2017/146351

    Article  Google Scholar 

  168. Batula AM, Ayaz H, Kim YE (2014) Evaluating a four class motor-imagery-based optical brain-computer interface. In: Proceedings of the 36th annual international conference of the IEEE engineering in medicine and biology society (EMBC ’14), Chicago, Ill, USA, pp 2000–2003

  169. Canning C, Scheutz M (2013) Functional near-infrared spectroscopy in human-robot interaction. J Human-Robot Inter 2(3):62–84

    Google Scholar 

  170. Kishi S, Luo Z, Nagano A, Okumura M, Nagano Y, Yamanaka Y (2008) On NIRS-based BRI for a human-interactive robot RI-MAN. In: Proceedings of the in joint 4th international conference on soft computing and intelligent systems and 9th international symposium on advanced intelligent systems (SCIS & ISIS), pp 124–129

  171. Takahashi K, Maekawa S, Hashimoto M (2014) Remarks on fuzzy reasoning-based brain activity recognition with a compact near infrared spectroscopy device and its application to robot control interface. In: Proceedings of the 2014 international conference on control, decision and information technologies, CoDIT 2014, pp 615–620

  172. Tumanov K, Goebel R, Mockel R, Sorger B, Weiss G (2015) FNIRS-based BCI for robot control (demonstration). In: Proceedings of the 14th international conference on autonomous agents and multiagent systems, AAMAS 2015, pp 1953–1954

  173. Blatt R, Ceriani S, Seno BD, Fontana G, Matteucci M, Migliore D (2008) Intelligent autonomous systems. Chapter 10. IOS Press, Amsterdam

    Google Scholar 

  174. Li Y, Pan J, Wang F, Yu Z (2013) A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control. IEEE Trans Biomed Eng 60(11):3156–3166

    Google Scholar 

  175. Duan F, Lin D, Li W, Zhang Z (2015) Design of a multimodal EEG-based hybrid BCI system with visual servo module. IEEE Trans Auton Ment Dev 7(4):332–341

    Google Scholar 

  176. Finke A, Knoblauch A, Koesling H, Ritter H (2011) A hybrid brain interface for a humanoid robot assistant. In: Proceedings of the 33rd annual international conference of the IEEE engineering in medicine and biology society (EMBS ’11), pp 7421–7424

  177. Robinson N, Vinod AP (2016) Noninvasive brain-computer interface: decoding arm movement kinematics and motor control. IEEE Syst Man Cybern Mag 2(4):4–16

    Google Scholar 

  178. Pohlmeyer EA, Mahmoudi B, Geng S, Prins N, Sanchez JC (2012) Brain-machine interface control of a robot arm using actor-critic rainforcement learning. In: Proceedings of the 34th annual international conference of the IEEE engineering in medicine and biology society (EMBS ’12), pp 4108–4111

  179. Elstob D, Secco EL (2016) A low cost EEG based BCI prosthetic using motor imagery. Int J Inf Technol Conver Serv 6(1):23–36

    Google Scholar 

  180. Sequeira S, Diogo C, Ferreira FJTE (2013) EEG-signals based control strategy for prosthetic drive systems. In: 2013 IEEE 3rd Portuguese meeting in bioengineering (ENBENG), Braga, pp 1–4

  181. Wang C, Xia B, Li J, Yang W, Xiao D, Cardona VA, Yang H (2011) Motor imagery BCI-based robot arm system. In: Proceedings of the 7th international conference on natural computation (ICNC ’11), pp 181–184

  182. Úbeda A, Azorín JM, García N, Sabater JM, Pérez C (2012) Brain-machine interface based on EEG mapping to control an assistive robotic arm. In: 2012 4th IEEE RAS & EMBS international conference on biomedical robotics and biomechatronics (BioRob), Rome, pp 1311–1315

  183. Úbeda A, Iáñez E, Badesa J, Morales R, Azorín JM, García N (2012). Control strategies of an assistive robot using a Brain-Machine Interface. In: 2012 IEEE/RSJ international conference on intelligent robots and systems, Vilamoura, pp 3553–3558

  184. Palankar M, De Laurentis KJ, Alqasemi R, Veras E, Dubey R, Arbel Y, Donchin E (2009) Control of a 9-DoF wheelchair-mounted robotic arm system using a P300 brain computer interface: initial experiments. In: Proceedings of the IEEE international conference on robotics and biomimetics (ROBIO ’08), pp 348–353

  185. Tanwani AK, Millán JR, Billard A (2014) Rewards-driven control of robot arm by decoding EEG signals. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society, Chicago, IL, pp 1658–1661

  186. Postelnicu CC, Talaba D, Toma MI (2011) Controlling a robotic arm by brainwaves and eye movement. In: Camarinha-Matos LM (eds) Technological innovation for sustainability. DoCEIS

  187. Iáñez E, Furió MC, Azorín JM, Huizzi JA, Fernández E (2009) Brain-robot interface for controlling a remote robot arm. In: Bioinspired applications in artificial and natural computation, IWINAC 2009, Lecture Notes in Computer Science, vol 5602

  188. Zhang W, Sun F, Liu C, Su W, Tan C, Liu S (2017) A hybrid EEG-based BCI for robot grasp controlling. In: 2017 IEEE international conference on systems, man, and cybernetics (SMC), Banff, AB, 2017, pp 3278–3283

  189. Xu Y, Ding C, Shu X, Gui K, Bezsudnova Y, Sheng X, Zhang D (2019) Shared control of a robotic arm using non-invasive brain-computer interface and computer vision guidance. Robot Auton Syst 115:121–129

    Google Scholar 

  190. Nisar H, Khow H-W, Yeap K-H (2018) Brain computer interface: controlling a robotic arm using facial expressions. Turkish J Electr Eng Comput Sci. https://doi.org/10.3906/elk-1606-296

    Article  Google Scholar 

  191. Sharma K, Jain N, Pal P (2017) Telemanipulation of a robotic arm using EEG artifacts. Int J Mechatron Electr Comput Technol 7:3595–3609

    Google Scholar 

  192. Zeng H, Wang Y, Wu C, Song A, Liu J, Ji P, Xu B, Zhu L, Li H, Wen P (2017) Closed-loop hybrid gaze brain-machine interface based robotic arm control with augmented reality feedback. Front Neurorobot 11:60. https://doi.org/10.3389/fnbot.2017.00060

    Article  Google Scholar 

  193. Mishchenko Y, Kaya M, Ozbay E, Yanar H (2017) Developing a 3-to 6-state EEG-based brain-computer interface for a robotic manipulator control. IEEE Trans Biomed Eng. https://doi.org/10.1109/TBME.2018.2865941

    Article  Google Scholar 

  194. Rao R, Scherer R (2010) Brain-computer interfacing [in the spotlight]. Signal Process Mag IEEE 27(4):150–152

    Google Scholar 

  195. Samek W, Muller K-R, Kawanabe M, Vidaurre C (2012) Brain computer interfacing in discriminative and stationary subspaces. In: Engineering in medicine and biology society (EMBC), 2012 annual international conference of the IEEE. IEEE, pp 2873–876

  196. Sosa OP, Quijano Y, Doniz M, Chong-Quero J (2011) BCI: a historical analysis and technology comparison. In: Health care exchanges (PAHCE). 2011 Pan American. IEEE, pp 205–09

  197. Zuquete A, Quintela B, Cunha JPS (2010) Biometric authentication using brain responses to visual stimuli. In: Proceedings of the international conference on bio-inspired systems and signal processing, pp 103–112

  198. Ortner R, Bruckner M, Prückl R, Grünbacher E, Costa U, Opisso E, Medina J, Guger C (2011) Accuracy of a P300 speller for people with motor impairments: a comparison. In: Proceedings of IEEE symposium computing intelligence, cognitive algorithms, mind, brain, pp 1–6

  199. Kaysa WA, Suprijanto S, Widyotriatmo A (2013) Design of Brain-computer interface platform for semi real-time commanding electrical wheelchair simulator movement. Proc Int Conf Instrumen Control Autom ICA 2013:39–44

    Google Scholar 

  200. Long J, Li Y, Wang H, Yu T, Pan J, Li F (2012) A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Trans Neural Syst Rehabil Eng 20(5):720–729

    Google Scholar 

  201. Panoulas KJ, Hadjileontiadis LJ, Panas SM (2010) Brain–computer interface (BCI): types, processing perspectives and applications. In: Multimedia services in intelligent environments. pp 299–321

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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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