Robotic Smart Prosthesis Arm with BCI and Kansei / Kawaii / Affective Engineering Approach. Pt I: Quantum Soft Computing Supremacy

Authors

  • Alexey V Nemchaninov
  • Alena V Nikolaeva
  • Sergey Victorovich Ulyanov Dubna State University
  • Andrey G Reshetnikov

DOI:

https://doi.org/10.30564/aia.v2i2.1567

Abstract

A description of the design stage and results of the development of the conceptual structure of a robotic prosthesis arm is given. As a result, a prototype of manmade prosthesis on a 3D printer as well as a foundation for computational intelligence presented. The application of soft computing technology (the first step of IT) allows to extract knowledge directly from the physical signal of the electroencephalogram, as well as to form knowledge-based intelligent robust control of the lower performing level taking into account the assessment of the patient’s emotional state. The possibilities of applying quantum soft computing technologies (the second step of IT) in the processes of robust filtering of electroencephalogram signals for the formation of mental commands and quantum supremacy simulation of robotic prosthetic arm discussed.

Keywords:

robotic prosthetic arm; cognitive computational intelligence; «brain-computer-device» neurointerface; mental commands; quantum soft computing; fuzzy cognitive controller

References

[1] Robbin A. Miranda, Casebeer William D., Heinc Amy M., et all. DARPA-funded efforts in the development of novel brain–computer interface technologies [J]. Journal of Neuroscience Methods 244. – 2015. Pp. 52–67

[2] An analytical review of the global robotics market 2019 [M]. Sberbank Robotic Lab., Sberbank, 2019. 272 с.

[3] Guang-Zhong Yang, Jim Bellingham, Pierre E. Dupont et al. The grand challenges of Science Robotics [J]. Sci. Robot. 3, eaar7650 (2018). Pp. 1-14.

[4] An analytical review of the global robotics market [R]. Laboratory of Robotics, Sberbank. 2018.79 p.

[5] Artificial Intelligence: Approaches to Formation. AI development strategies in the Russian Federation [R]. Sberbank, 2019.

[6] E.K. Amirova, V.A. Efimov, S.V. Ulyanovet all. Expert system for selecting lower-extremity (thigh) prostheses and diagnosis of the quality of artificial replacement. Pt 1 [J], ISSN 0006-3398, Consultants bureau, New York, J. of Biomedical Engineering, May-June, 1991, № 3, pp. 26-31.

[7] Amirova E.K., Efimov V.A., Kuzhekin A.P., Ulyanov S.V. and etc., “An expert system for selecting lower-extremity (thigh) prosthesis and evaluation of prosthetic quality. Part 2 [J]”, ISSN 0006-3398, Consultants bureau, New York, J. of Biomedical Engineering, November-December, 1991, № 6, pp. 5-12.

[8] Lupina I.V., Slepchenko A.N., Ulyanov S.V., and etc., Hybrid expert system with an in-depth representation of knowledge for the design and diagnosis of biotechnological products [J] – Izv. USSR Academy of Sciences. Ser. Technical cybernetics”, 1991, No. 5, C. 152-175.

[9] Kaplan A., Shishkin S., Ganin I., and etc., The prospects of the P300-based brain computer interface in game control [M] – «IEEE Transactions on Computational Intelligence and AI in Games», 2013

[10] Shishkin S.L., Ganin I.P., Kaplan A.Y. Event-related potentials in a moving matrix modification of the P300 brain-computer interface paradigm [J] – «Neuroscience Letters», 2011, v. 496 (2), p. 95-99.

[11] Leigh R. Hochberg, Mijail D. Serruya, Gerhard M. Friehs, and etc., Neuronal ensemble control of prosthetic devices by a human with tetraplegia [J] – «Nature», 2006, v. 442, p. 164-171.

[12] Höller Y., Bergmann J., Kronbichler M. and etc., E. Real movement vs. motor imagery in healthy subjects [J] – «Int. J. Psychophysiol», 2012, p. S0167-8760.

[13] Salvaris M., Cinel C., Citi L., and etc,. Novel protocols for P300-based brain-computer interfaces [J] – «IEEE Trans. Neural. Syst. Rehabil. Eng.», 2012, v. 20, p. 8-17

[14] Musallam S., Corneil B.D., Greger B., and etc., Cognitive Control Signals for Neural Prosthetics [J] – «Science», 2004, v. 305, p. 258-262

[15] Ivanitsky G. A., Determining the nature of mental activity according to the rhythmic pattern of the EEG] [A] [available URL: https://www.rfbr.ru/rffi/portal/project_search/o_53385 (in Russ)]

[16] Sunny T.D., Aparna T., Neethu P., and etc., Robotic Arm with Brain Computer Interfacing [M] – International Conference on Emerging Trends in Engineering, Science and Technology (ICETEST - 2015)

[17] Gandhi V. Brain-Computer Interfacing for Assistive Robotics 1st Edition: Electroencephalograms, Recurrent Quantum Neural Networks, and User-Centric Graphical Interfaces. [J] N.Y. Academic Press. 2015.

[18] Meng Jing, Brain Co CEO says his ‘mind-reading’ tech is here to improve concentration, not surveillance, [M] South China Morning Post May, 2019. [available URL: https://www.scmp.com/tech/innovation/article/3008439/brainco-ceo-says-his-mind-reading-tech-here-improve-concentration ]

[19] Kazuki Yanagisawa, Hitoshi Tsunashima and Kaoru Sakatani, Brain-Computer Interface Using Near Infrared Spectroscopy for Rehabilitation, Infrared Spectroscopy [J] – Life and Biomedical Sciences, Prof. Theophile (Ed.), ISBN: 978-953-51-0538-1, InTech. [available URL: https://www.intechopen.com/books/infrared-spectroscopy-life-and-biomedical-sciences/brain-computer-interface-using-near-infrared-spectroscopy-for-rehabilitation ]

[20] Vaughan TM, McFarland DJ, Schalk G, and etc., The Wadsworth BCI Research and Development Program: at home with BCI [J] – IEEE Trans Neural Syst. Eng. – 2006. – Vol. 14. – № 2. – Pp. 229-233.

[21] Ulyanov S. V., Shevchenko A. V., Mamaeva A. A., and etc, Soft Computing Optimizer as Deep Machine Learning in Intelligent Cognitive Estimation of Human Being Emotion for Robotic Control [J] – Robots and Engineering Cybernetics (2019).

[22] D. Purves, G. J. Augustine, D. Fitzpatrick, and etc., Neuroscience [M] – Sinauer Associates, (2001).

[23] J. Moren, Emotion and Learning – A Computational Model of the Amygdala [D], Lund University, Lund, Sweden, (2002).

[24] J. Moren, C. Balkenius, A Computational Model of Emotional Learning in the Amygdala [J], Cybernetics and Systems, Vol. 32, No. 6, (2000), pp. 611-636.

[25] C. Lucas, D. Shahmirzadi, N. Sheikholeslami, Introducing BELBIC: Brain Emotional Learning Based Intelligent Controller [J], International Journal of Intelligent Automation and Soft Computing, Vol. 10, No.1, (2004), pp. 11-22.

[26] H. Rouhani, M. Jalili, B. N. Araabi, W. Eppler and C. Lucas, Brain emotional learning based intelligent controller applied to neuro-fuzzy model of micro-heat exchanger [J], Expert Systems with Applications, Vol. 32, (2007), pp. 911-924.

[27] Rajesh P. N. Rao, Paul G. Allen, Towards Neural Co-Processors for the Brain: Combining Decoding and Encoding in Brain-Computer Interfaces [J], Invited submission to the journal Current Opinion in Neurobiology, 2018

[28] José del R. Millán, Pierre W. Ferrez, Anna Buttfield, Non Invasive Brain-Machine Interfaces [A], IDIAP Research Institute [available URL: https://www.esa.int/gsp/ACT/doc/ARI/ARI%20Study%20Report/ACT-RPT-BIO-ARI-056402-Non_invasive_brain-machine_interfaces_-_Martigny_IDIAP.pdf ]

[29] Maksimenko V. A., Kurkin S. A., Pitsik E. N., Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity [A], Hindawi Complexity Volume 2018, Article ID 9385947, 10 pages, [availableURL: https://doi.org/10.1155/2018/9385947 ]

[30] US Patent No 7,219,087B2, "Soft computing optimizer of intelligent control system structures" [P] (Inventor: S. V. Ulyanov), Date of patent: May 15, 2007 [WO 2005/013019 A3, 2005].

[31] US Patent No 2006, 0218 A1, "System for soft computing simulation" [P] (Inventor: S. V. Ulyanov), Date of patent: Sept. 2006, 2006.

[32] Nikolaeva A. V., Ul’yanov S.V. Intelligent robust control of an autonomous robot-manipulator.[A] – Software systems and computational methods. - 2014. - № 1. - pp. 34-62. DOI: 10.7256/2305-6061.2014.1.11466 [available URL: https://en.nbpublish.com/library_get_pdf.php?id=28019 ]

[33] Tarasov V.B. From multi-agent systems to intelligent organizations: philosophy, psychology, computer science [J]. – M.: URSS editorial, 2002. – Pp. 352. (in Russ).

[34] Zaitsev A.A., Kureichik V.V., Polupanov A.A. Overview of evolutionary optimization techniques based on swarm intelligence [J] – News SFU. Technical science, 2010. – No12. (in Russian).

[35] Kureichik V.M., Kazharov A.A., The use of swarm intelligence in solving difficult problems [J] – News SFU. Technicalscience, 2011. – No 7. (in Russ).

[36] Sandberg H. et all. Maximum work extraction and implementation costs for nonequilibrium Maxwell’s demon [J] Physical Review E. 2014. No 4. pp. 042119.

[37] Chatzis S. P., Korkinof D., Demiris Y., A quantum-statistical approach toward robot learning by demonstration [J] – IEEE Transactions on Robotics. – 2012. – Vol. 28. – № 6. – Pp. 1371-1381.

[38] Sagawa T, Ueda M. Minimal Energy Cost for Thermodynamic Information Processing: Measurement and Information Erasure [J] – Phys. Rev. Lett. – 2009. – Vol. 102. – No 25. – Pp. 250602.–Erratum. Phys. Rev. Lett. 106, 189901. – 2011.

[39] Horowitz J. M., Sandberg H. Second-law-like inequalities with information and their interpretations [J] – New Journal of Physics. –2014. – Vol. 16. – Pp. 125007.

[40] Ulyanov S. V., Reshetnikov A. G., Mamaeva A. A., and etc., Hybrid cognitive control systems on the example of driving [J] – Journal of Systems Analysis in Science and Education – 2010. – No. 3. [availableURL: http://sanse.ru/download/261.] (in Russ.).

[41] Ulyanov S. V., Reshetnikov A. G. Basis of cognitive computer training in robotics. Intellectual simulator for the formation of active knowledge [J] – Journal of Systems Analysis in Science and Education – 2016. – No. 4. [availableURL: http://sanse.ru/download/277.] (in Russ.).

[42] Gael Langevin, InMoov is the first Open Source 3D printed life-size robot [R] [available URL: http://inmoov.fr.]

[43] Hand robot InMoov, opensource - project. [R] [available URL: https://www.thingiverse.com/thing:17773 ]

[44] EmotivEpoc, Neurocomputer interface [R][available URL: https://www.emotiv.com/ ]

[45] Arduino, electronic device design tool. [R][available URL: http://arduino.ru/ ]

[46] Petrov B.N., Ulanov G.M., Ulyanov S.V. etc., The theory of models in control processes: information and thermodynamic aspects [J] – M.: Nauka, 1978. - 224 p. (in Russ.).

[47] Sotnokov P., I., Overview of EEG Signal Processing Techniques at Brain Computer Interfaces [J] – Engineering Bulletin, Electronic Journal. – 2014. – No.10. (in Russ.).

[48] Ulyanov S.V. and etc. Intelligent self-organizing cognitive regulators. Part 2: models of cognitive interfaces “brain - device” [J] – Journal of Systems Analysis in Science and Education – 2015. – No. 1. [availableURL: http://sanse.ru/download/236 ] (in Russ.).

[49] Ulyanov S.V., Intelligent self-organized robust control design based on quantum / soft computing technologies and Kansei engineering [J] / Computer Science Journal of Moldova. 2013. Vol.21. №2(62), pp. 242-279.

[50] Ulyanov S.V., Yamafuji K., Intelligent self-organized cognitive controllers. Pt. 1: Kansei / affective engineering and quantum / soft computing technologies [J] – System Analysis in Science and Education. 2014. No 4., [available URL: http:/www.sanse.ru/archive/48 ]

[51] Ulyanov S.V., Quantum fast algorithm computational intelligence Pt I: SW / HW smart toolkit – Artificial Intelligence Advances. 2019. Vol. 1. No 1. Pp. 18-43 [availableURL: https://doi.org/10.30564/aia.v1i1.619].

[52] Ulyanov S.V., Reshetnikov A.G., Tyatyushkina O.Yu. Intelligent robotics, part 2: socio-economic-technical platform of the cognitive educational process [J] – Journal of Systems Analysis in Science and Education. 2019. No 4. [available URL: http://sanse.ru/download/277 ] (in Russ.).

[53] D. Shahmirzadi, Computational Modeling of the Brain Limbic System and its Application In Control Engineering [D], Texas A&M University, U.S.A., (2005).

[54] Reza Keramat, Mohammad Hosein Ershadi, Shahrokh Shojaeian. A Comparison of Fuzzy and Brain Emotional Learning-Based Intelligent Control Approaches for a Full Bridge DC-DC Converter [J] – International Journal of Industrial Electronics, Control and Optimization, Vol. 2, No. 3, pp. 197-206, July (2019).

Downloads

How to Cite

Nemchaninov, A. V., Nikolaeva, A. V., Ulyanov, S. V., & Reshetnikov, A. G. (2020). Robotic Smart Prosthesis Arm with BCI and Kansei / Kawaii / Affective Engineering Approach. Pt I: Quantum Soft Computing Supremacy. Artificial Intelligence Advances, 2(2), 68–87. https://doi.org/10.30564/aia.v2i2.1567

Issue

Vol. 2 , Iss. 2 (October 2020)

Article Type

Article
Crossref
Scopus
Google Scholar
Europe PMC