Development of a Real-Time Brain-Computer Interface for Interactive Robot Therapy: An Exploration of EEG and EMG Features during Hypnosis

This study presents a framework for development of a
new generation of therapy robots that can interact with users by
monitoring their physiological and mental states. Here, we focused
on one of the controversial methods of therapy, hypnotherapy.
Hypnosis has shown to be useful in treatment of many clinical
conditions. But, even for healthy people, it can be used as an
effective technique for relaxation or enhancement of memory and
concentration. Our aim is to develop a robot that collects information
about user’s mental and physical states using electroencephalogram
(EEG) and electromyography (EMG) signals and performs costeffective
hypnosis at the comfort of user’s house. The presented
framework consists of three main steps: (1) Find the EEG-correlates
of mind state before, during, and after hypnosis and establish a
cognitive model for state changes, (2) Develop a system that can
track the changes in EEG and EMG activities in real time and
determines if the user is ready for suggestion, and (3) Implement our
system in a humanoid robot that will talk and conduct hypnosis on
users based on their mental states. This paper presents a pilot study in
regard to the first stage, detection of EEG and EMG features during
hypnosis.




References:
[1] J. B. Weinberg, and X. Yu, “Robotics in education: Low-cost platforms
for teaching integrated systems,” Robotics & Automation Magazine,
IEEE, 10(2), pp. 4-6, 2003.
[2] B. Robins, K. Dautenhahn, R. Te Boekhorst, and A. Billard, “Robotic
assistants in therapy and education of children with autism: Can a small
humanoid robot help encourage social interaction skills?” Universal
Access in the Information Society, 4(2), pp. 105-120, 2005.
[3] T. Kanda, R. Sato, N. Saiwaki, and H. Ishiguro, “A two-month field trial
in an elementary school for long-term human–robot interaction,”
Robotics, IEEE Transactions on, 23(5), pp. 962-971, 2007.
[4] L. Geppert, Qrio, the robot that could. Ieee Spectrum, 41(5), pp. 34-37,
2004.
[5] M. Fujita, and R. Enteretainment, “Entertainment Robot: AIBO,” The
journal of the Institute of Image Information and Television Engineers,
54(5), pp. 657-661, 2000.
[6] H. I. Krebs, e., “Rehabilitation robotics: Performance-based progressive
robot-assisted therapy,” Autonomous Robots, 15(1), pp. 7-20, 2003.
[7] T. Mukai, et al., “Development of a nursing-care assistant robot riba that
can lift a human in its arms,” In Intelligent Robots and Systems (IROS),
2010 IEEE/RSJ International Conference, pp. 5996-6001. October 2010.
[8] T. Nef, and R. Riener, “ARMin-design of a novel arm rehabilitation
robot,” In Rehabilitation Robotics, ICORR 2005. 9th International
Conference, pp. 57-60, IEEE, June 2005.
[9] K. Wada, T. Shibata, T. Saito, and K. Tanie, “Effects of robot-assisted
activity for elderly people and nurses at a day service center,”
Proceedings of the IEEE, 92(11), pp. 1780-1788, 2004.
[10] S. Shamsuddin, H. Yussof, L. Ismail, F. A. Hanapiah, S. Mohamed, H.
A. Piah, and N. I. Zahari, “Initial response of autistic children in humanrobot
interaction therapy with humanoid robot NAO,” In Signal
Processing and its Applications (CSPA), 2012 IEEE 8th International
Colloquium, pp. 188-193, IEEE, March 2012.
[11] H. Kozima, C. Nakagawa, and Y. Yasuda, “Interactive robots for
communication-care: A case-study in autism therapy,” In Robot and
Human Interactive Communication, ROMAN 2005. IEEE International
Workshop, pp. 341-346, August 2005.
[12] R. Yamazaki, S. Nishio, H. Ishiguro, M. Nørskov, N. Ishiguro, and G.
Balistreri, “Social acceptance of a teleoperated android: Field study on
elderly’s engagement with an embodied communication medium in
denmark,” In Social Robotics, pp. 428-437, Springer Berlin Heidelberg,
2012.
[13] T. Fong, I. Nourbakhsh, and K. Dautenhahn, “A survey of socially
interactive robots,” Robotics and autonomous systems, 42(3), pp. 143-
166, 2003.
[14] P. London, J. T. Hart, and M. P. Leibovitz, “EEG alpha rhythms and
susceptibility to hypnosis,” Nature, 1968.
[15] M. E. Sabourin, S. D. Cutcomb, H. J. Crawford, and K. Pribram, “EEG
correlates of hypnotic susceptibility and hypnotic trance: Spectral
analysis and coherence,” International Journal of Psychophysiology,
10(2), pp. 125-142, 1990.
[16] R. Freeman, A. Barabasz, M. Barabasz, and D. Warner, “Hypnosis and
distraction differ in their effects on cold pressor pain,” American Journal
of Clinical Hypnosis, 43(2), pp. 137-148, 2000.
[17] J. D. Williams, and J. H. Gruzelier, “Differentiation of hypnosis and
relaxation by analysis of narrow band theta and alpha frequencies,” International Journal of Clinical and Experimental Hypnosis, 49(3), pp.
185-206, 2001.
[18] N. F. Graffin, W. J. Ray, and R. Lundy, “EEG concomitants of hypnosis
and hypnotic susceptibility,” Journal of Abnormal Psychology, 104(1),
pp. 123-131, 1995.
[19] G. Ádám, I. Mészáros, and É. I. Bányai, eds. Brain and behaviour:
proceedings of the 28th International Congress of Physiological
Sciences, Budapest, 1980. Vol. 17. Elsevier, 2013.
[20] http://www.sccn.ucsd.edu/eeglab (Accessed on 20/12/2016)
[21] A. A. Fingelkurts, A. A. Fingelkurts, S. Kallio, and A. Revonsuo,
“Cortex functional connectivity as a neurophysiological correlate of
hypnosis: an EEG case study,” Neuropsychologia 45.7, pp. 1452-1462,
2007
[22] P., Sauseng, and W. Klimesch, “What does phase information of
oscillatory brain activity tell us about cognitive processes?”
Neuroscience & Biobehavioral Reviews, 32(5), pp. 1001-1013, 2008.
[23] W. Klimesch, M. Doppelmayr, A. Yonelinas, N. E. A. Kroll, M.
Lazzara, D. Röhm, and W. Gruber, “Theta synchronization during
episodic retrieval: neural correlates of conscious awareness,” Cognitive
Brain Research, 12(1), pp. 33-38, 2001.
[24] T. Fernández, et al., “EEG activation patterns during the performance of
tasks involving different components of mental calculation,”
Electroencephalography and clinical Neurophysiology, 94(3), pp. 175-
182, 1995.
[25] V. Galea, E. Z. Woody, H. Szechtman, and M. R. Pierrynowski,
“Motion in response to the hypnotic suggestion of arm rigidity: A
window on underlying mechanisms,” Intl. Journal of Clinical and
Experimental Hypnosis, 58(3), pp. 251-268, 2010.
[26] M. J. Batty, S. Bonnington, B. K. Tang, M. B. Hawken, and J. H.
Gruzelier, “Relaxation strategies and enhancement of hypnotic
susceptibility: EEG neurofeedback, progressive muscle relaxation and
self-hypnosis,” Brain research bulletin, 71(1), pp. 83-90, 2006.
[27] J. H. Gruzelier, “EEG-neurofeedback for optimising performance. I: a
review of cognitive and affective outcome in healthy participants,”
Neuroscience & Biobehavioral Reviews, 44, pp. 124-141, 2014.
[28] K. Thornton, “Improvement/rehabilitation of memory functioning with
neurotherapy/QEEG biofeedback,” The Journal of head trauma
rehabilitation, 15(6), pp. 1285-1296, 2000.
[29] B. H. Cho, et al., “Neurofeedback training with virtual reality for
inattention and impulsiveness,” Cyberpsychology & Behavior, 7(5), pp.
519-526, 2004.