Table of Contents Author Guidelines Submit a Manuscript
Wireless Communications and Mobile Computing
Volume 2017, Article ID 2986423, 16 pages
https://doi.org/10.1155/2017/2986423
Research Article

Wireless Brain-Robot Interface: User Perception and Performance Assessment of Spinal Cord Injury Patients

1Biomedical Electronics Robotics & Devices (BERD) Group, Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
21st Department of Neurosurgery, “AHEPA” University General Hospital, Aristotle University of Thessaloniki (AUTH), 54636 Thessaloniki, Greece
3Robotics Laboratory, Computer Science Department, American College of Thessaloniki (ACT), 55535 Thessaloniki, Greece

Correspondence should be addressed to Alkinoos Athanasiou; rg.htua@sooniklahta

Received 25 August 2017; Accepted 10 December 2017; Published 31 December 2017

Academic Editor: Kyriaki Kalimeri

Copyright © 2017 Alkinoos Athanasiou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. L. R. Hochberg, D. Bacher, B. Jarosiewicz et al., “Reach and grasp by people with tetraplegia using a neurally controlled robotic arm,” Nature, vol. 485, no. 7398, pp. 372–375, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. J. L. Collinger, B. Wodlinger, J. E. Downey et al., “High-performance neuroprosthetic control by an individual with tetraplegia,” The Lancet, vol. 381, no. 9866, pp. 557–564, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. A. R. C. Donati, S. Shokur, E. Morya et al., “Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients,” Scientific Reports, vol. 6, no. 1, p. 30383, 2016. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Alam, W. Rodrigues, B. N. Pham, and N. V. Thakor, “Brain-machine interface facilitated neurorehabilitation via spinal stimulation after spinal cord injury: Recent progress and future perspectives,” Brain Research, vol. 1646, pp. 25–33, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. R. R. Harrison, R. J. Kier, C. A. Chestek et al., “Wireless neural recording with single low-power integrated circuit,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 17, no. 4, pp. 322–329, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. A. V. Nurmikko, J. P. Donoghue, L. R. Hochberg et al., “Listening to brain microcircuits for interfacing with external world—progress in wireless implantable microelectronic neuroengineering devices,” Proceedings of the IEEE, vol. 98, no. 3, pp. 375–388, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. J. L. Collinger, S. Foldes, T. M. Bruns, B. Wodlinger, R. Gaunt, and D. J. Weber, “Neuroprosthetic technology for individuals with spinal cord injury,” The Journal of Spinal Cord Medicine, vol. 36, no. 4, pp. 258–272, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. P. H. Peckham and J. S. Knutson, “Functional electrical stimulation for neuromuscular applications,” Annual Review of Biomedical Engineering, vol. 7, pp. 327–360, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. L. Pignolo, “Robotics in neuro-rehabilitation,” Journal of Rehabilitation Medicine, vol. 41, no. 12, pp. 955–960, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. B. He, Ed., Modeling, Imaging of Bioelectrical Activity, Springer US, Boston, MA, USA, 2005.
  11. P. L. Nunez and R. Srinivasan, “Electric Fields of the Brain: The neurophysics of EEG,” Electric Fields of the Brain: The neurophysics of EEG, pp. 1–611, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Athanasiou, I. Xygonakis, N. Pandria et al., “Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms,” BioMed Research International, vol. 2017, pp. 1–17, 2017. View at Publisher · View at Google Scholar
  13. S. Baillet, “Magnetoencephalography for brain electrophysiology and imaging,” Nature Neuroscience, vol. 20, no. 3, pp. 327–339, 2017. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Meng, S. Zhang, A. Bekyo, J. Olsoe, B. Baxter, and B. He, “Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks,” Scientific Reports, vol. 6, no. 1, article 38565, 2016. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Kim and A. M. Cook, “Manipulation and Mobility Aids,” in Electronic Devices for Rehabilitation, J. G. Webster, Ed., Wiley, London, 1985. View at Google Scholar
  16. M. Hillman, “2 Rehabilitation Robotics from Past to Present A Historical Perspective,” in Advances in Rehabilitation Robotics, pp. 25–44, Springer Berlin Heidelberg, 2006. View at Google Scholar
  17. W. Seamone and G. Schmeisser, Evaluation of the APL/JHU Robot Arm Work Station, 1986.
  18. C. P. Mason and E. Peizer, “Medical Manipulator for Quadriplegics, Colloques IRIA,” in Proceedings of the in International Conference on Telemanipulators for the Physically Handicapped, pp. 309–312, 1978.
  19. H. H. Kwee, M. Tramblay, R. Barbier et al., “First experimentation of the spartacus telethesis in a clinical environment,” Paraplegia, vol. 21, no. 5, pp. 275–286, 1983. View at Publisher · View at Google Scholar · View at Scopus
  20. Y. Wakita, W.-K. Yoon, and N. Yamanobe, “User evaluation to apply the robotic arm RAPUDA for an upper-limb disabilities Patient's Daily Life,” in Proceedings of the 2012 IEEE International Conference on Robotics and Biomimetics, ROBIO 2012, pp. 1482–1487, China, December 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. V. Maheu, P. S. Archambault, J. Frappier, and F. Routhier, “Evaluation of the JACO robotic arm: Clinico-economic study for powered wheelchair users with upper-extremity disabilities,” in Proceedings of the Rehab Week Zurich 2011 - 2011 IEEE International Conference on Rehabilitation Robotics, ICORR 2011, Switzerland, July 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. M. S. Johannes, J. D. Bigelow, J. M. Burck, S. D. Harshbarger, M. V. Kozlowski, and T. Van Doren, “An overview of the developmental process for the modular prosthetic limb,” Johns Hopkins APL Technical Digest, vol. 30, no. 3, pp. 207–216, 2011. View at Google Scholar · View at Scopus
  23. J. J. Daly and J. R. Wolpaw, “Brain-computer interfaces in neurological rehabilitation,” The Lancet Neurology, vol. 7, no. 11, pp. 1032–1043, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Gomez-Rodriguez, M. Grosse-Wentrup, J. Hill, A. Gharabaghi, B. Scholkopf, and J. Peters, “Towards brain-robot interfaces in stroke rehabilitation,” in Proceedings of the IEEE International Conference on Rehabilitative Robotics, vol. 2011, 2011. View at Scopus
  25. K. K. Ang, C. Guan, K. S. Chua et al., “A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '09), pp. 5981–5984, Minneapolis, Minn, USA, September 2009. View at Publisher · View at Google Scholar
  26. K. Dautenhahn, “Methodology & themes of human-robot interaction: a growing research field,” International Journal of Advanced Robotic Systems, vol. 4, no. 1, pp. 103–108, 2007. View at Google Scholar
  27. C. Bartneck, D. Kulić, E. Croft, and S. Zoghbi, “Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots,” International Journal of Social Robotics, vol. 1, no. 1, pp. 71–81, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. O. Christ and M. Reiner, “Perspectives and possible applications of the rubber hand and virtual hand illusion in non-invasive rehabilitation: Technological improvements and their consequences,” Neuroscience & Biobehavioral Reviews, vol. 44, pp. 33–44, 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. G. Arfaras, A. Athanasiou, P. Niki et al., “Visual Versus Kinesthetic Motor Imagery for BCI Control of Robotic Arms (Mercury 2.0),” in Proceedings of the 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), pp. 440–445, Thessaloniki, June 2017. View at Publisher · View at Google Scholar
  30. A. Athanasiou, G. Arfaras, I. Xygonakis et al., “Commercial BCI Control and Functional Brain Networks in Spinal Cord Injury: A Proof-of-Concept,” in Proceedings of the 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), pp. 262–267, Thessaloniki, June 2017. View at Publisher · View at Google Scholar
  31. “Brainwave control of a wearable robotic arm for rehabilitation and neurophysiological study in cervical spine injury (CSI:Brainwave),” https://clinicaltrials.gov/ct2/show/NCT02443558.
  32. N. Moustakas, P. Kartsidis, A. Athanasiou, A. Astaras, and P. D. Bamidis, “Development of MERCURY version 2.0 robotic arms for rehabilitation applications,” in Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '15), July 2015. View at Publisher · View at Google Scholar · View at Scopus
  33. S. C. Kirshblum, S. P. Burns, F. Biering-Sorensen et al., “International standards for neurological classification of spinal cord injury (revised 2011),” The Journal of Spinal Cord Medicine, vol. 34, no. 6, pp. 535–546, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. R. W. Bohannon and M. B. Smith, “Interrater reliability of a modified Ashworth scale of muscle spasticity,” Physical Therapy in Sport, vol. 67, no. 2, pp. 206-207, 1987. View at Publisher · View at Google Scholar · View at Scopus
  35. J. T. C. Hsieh, D. L. Wolfe, W. C. Miller, and A. Curt, “Spasticity outcome measures in spinal cord injury: Psychometric properties and clinical utility,” Spinal Cord, vol. 46, no. 2, pp. 86–95, 2008. View at Publisher · View at Google Scholar · View at Scopus
  36. M. Itzkovich, I. Gelernter, and F. Biering-Sorensen, “The Spinal Cord Independence Measure (SCIM) version III: Reliability and validity in a multi-center international study,” Disability and Rehabilitation, vol. 29, no. 24, pp. 1926–1933, 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. A. Athanasiou, A. Alexandrou, E. Paraskevopoulos, N. Foroglou, A. Prassas, and P. D. Bamidis, “Towards a Greek adaptation of the Spinal Cord Independence Measure (SCIM),” in in proceedings of the 15th European Congress of Neurosurgery (EANS 14), pp. 181–184, 2015.
  38. D. F. Marks, “Directions in Mental-Imagery Research,” Journal of Mental Imagery, vol. 19, no. 3-4, pp. 153–167, 1995. View at Google Scholar
  39. A. T. Beck, R. A. Steer, and M. G. Garbin, “Psychometric properties of the Beck Depression Inventory: twenty-five years of evaluation,” Clinical Psychology Review, vol. 8, no. 1, pp. 77–100, 1988. View at Publisher · View at Google Scholar · View at Scopus
  40. W. Petersen, “Society and the Adolescent Self-Image. Morris Rosenberg. Princeton University Press, Princeton, N.J., 1965. xii + 326 pp. $6.50,” Science, vol. 148, no. 3671, p. 804, 1965. View at Publisher · View at Google Scholar
  41. M. Giannakou, P. Roussi, M.-E. Kosmides, G. Kiosseoglou, A. Adamopoulou, and G. Garyfallos, “Adaptation of the beck depression inventory-II to greek population,” Hellenic Journal of Psychology, vol. 10, no. 2, pp. 120–146, 2013. View at Google Scholar · View at Scopus
  42. C. Galanou, M. Galanakis, E. Alexopoulos, and C. Darviri, “Rosenberg Self-Esteem Scale Greek Validation on Student Sample,” Psychology, vol. 05, no. 08, pp. 819–827, 2014. View at Publisher · View at Google Scholar
  43. A. Astaras, A. Athanasiou, A. Alexandrou, P. Kartsidis, N. Moustakas, and P. D. Bamidis, “Double-blind greek translation and online implementation of the Godspeed robotics questionnaire,” in Proceedings of the in 6th Panhellenic Conference on Biomedical Technology Conference, p. 34, 2015.
  44. E. I. Konstantinidis, A. Billis, C. Bratsas, A. Siountas, and P. D. Bamidis, “Thessaloniki active and healthy ageing living lab: The roadmap from a specific project to a living lab towards openness,” in Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '2016), pp. 1–4, July 2016. View at Publisher · View at Google Scholar · View at Scopus
  45. E. I. Konstantinidis and P. D. Bamidis, “Density based clustering on indoor kinect location tracking: A new way to exploit active and healthy aging living lab datasets,” in Proceedings of the 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 1–6, November 2015. View at Publisher · View at Google Scholar · View at Scopus
  46. E. I. Konstantinidis, P. E. Antoniou, G. Bamparopoulos, and P. D. Bamidis, “A lightweight framework for transparent cross platform communication of controller data in ambient assisted living environments,” Information Sciences, vol. 300, no. 1, pp. 124–139, 2015. View at Publisher · View at Google Scholar · View at Scopus
  47. T. Sollfrank, D. Hart, R. Goodsell, J. Foster, and T. Tan, “3D visualization of movements can amplify motor cortex activation during subsequent motor imagery,” Frontiers in Human Neuroscience, vol. 9, article 463, 2015. View at Publisher · View at Google Scholar · View at Scopus
  48. D. Cramer, “Fundamental statistics for social research,” in Step-by-step calculations and computer techniques using SPSS for Windows, Routledge, London, UK, 1988. View at Publisher · View at Google Scholar
  49. D. Cramer and D. Howitt, The SAGE Dictionary of Statistics, SAGE Publications, 2004. View at Publisher · View at Google Scholar
  50. D. P. Doane and L. E. Seward, “Measuring skewness: a forgotten statistic?” Journal of Statistics Education, vol. 19, no. 2, pp. 1–18, 2011. View at Google Scholar · View at Scopus
  51. N. M. Razali, Y. B. Wah, and M. Sciences, “Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov,” Lilliefors and Anderson-Darling tests, vol. 2, no. 1, pp. 21–33, 2011. View at Google Scholar
  52. S. S. Shapiro and M. B. Wilk, “An analysis of variance test for normality: Complete samples,” Biometrika, vol. 52, pp. 591–611, 1965. View at Publisher · View at Google Scholar · View at MathSciNet
  53. N. Mogey, “So You Want to Use a Likert Scale?” in Edinburgh: 2 Evaluation Cookbook Learning Technology Dissemination Initiative, Institute for Computer Based Learning, Heriot-Watt University, 1998. View at Google Scholar
  54. N. Birbaumer and P. Sauseng, “Brain–Computer Interface in Neurorehabilitation,” 2010. View at Google Scholar
  55. A. Vuckovic, J. A. Pineda, K. LaMarca, D. Gupta, and C. Guger, “Interaction of BCI with the underlying neurological conditions in patients: pros and cons,” Frontiers in Neuroengineering, vol. 7, 2014. View at Publisher · View at Google Scholar
  56. F. Nijboer, N. Birbaumer, and A. Kübler, “The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study,” Frontiers in Neuroscience, vol. 4, article no. 55, 2010. View at Publisher · View at Google Scholar · View at Scopus
  57. E. M. Hammer, S. Halder, B. Blankertz et al., “Psychological predictors of SMR-BCI performance,” Biological Psychology, vol. 89, no. 1, pp. 80–86, 2012. View at Publisher · View at Google Scholar · View at Scopus
  58. C. Papadelis, C. Kourtidou-Papadeli, P. Bamidis, and M. Albani, “Effects of imagery training on cognitive performance and use of physiological measures as an assessment tool of mental effort,” Brain and Cognition, vol. 64, no. 1, pp. 74–85, 2007. View at Publisher · View at Google Scholar · View at Scopus
  59. F. González-Palau, M. Franco, P. D. Bamidis et al., “The effects of a computer-based cognitive and physical training program in a healthy and mildly cognitive impaired aging sample,” Aging & Mental Health, vol. 18, no. 7, pp. 838–846, 2014. View at Publisher · View at Google Scholar · View at Scopus
  60. S. M. Roldan, “Object Recognition in Mental Representations: Directions for Exploring Diagnostic Features through Visual Mental Imagery,” Frontiers in Psychology, vol. 8, 2017. View at Publisher · View at Google Scholar
  61. E. Broadbent, “Interactions with Robots: The Truths We Reveal about Ourselves,” Annual Review of Psychology, vol. 68, pp. 627–652, 2017. View at Publisher · View at Google Scholar · View at Scopus
  62. E. López-Larraz, F. Trincado-Alonso, V. Rajasekaran et al., “Control of an ambulatory exoskeleton with a brain-machine interface for spinal cord injury gait rehabilitation,” Frontiers in Neuroscience, vol. 10, 2016. View at Publisher · View at Google Scholar · View at Scopus
  63. G. E. Francisco, N. Yozbatiran, J. Berliner et al., “Robot-Assisted Training of Arm and Hand Movement Shows Functional Improvements for Incomplete Cervical Spinal Cord Injury,” American Journal of Physical Medicine & Rehabilitation, vol. 96, pp. S171–S177, 2017. View at Publisher · View at Google Scholar
  64. A. Jackson and J. B. Zimmermann, “Neural interfaces for the brain and spinal cord - Restoring motor function,” Nature Reviews Neurology, vol. 8, no. 12, pp. 690–699, 2012. View at Publisher · View at Google Scholar · View at Scopus
  65. R. Grech, T. Cassar, J. Muscat et al., “Review on solving the inverse problem in EEG source analysis,” Journal of NeuroEngineering and Rehabilitation, vol. 5, article 25, 2008. View at Publisher · View at Google Scholar · View at Scopus
  66. S. Baillet, J. C. Mosher, and R. M. Leahy, “Electromagnetic brain mapping,” IEEE Signal Processing Magazine, vol. 18, no. 6, pp. 14–30, 2001. View at Publisher · View at Google Scholar · View at Scopus
  67. C. J. Holmes, R. Hoge, L. Collins, R. Woods, A. W. Toga, and A. C. Evans, “Enhancement of MR images using registration for signal averaging,” Journal of Computer Assisted Tomography, vol. 22, no. 2, pp. 324–333, 1998. View at Publisher · View at Google Scholar · View at Scopus
  68. B. J. Edelman, B. Baxter, and B. He, “EEG source imaging enhances the decoding of complex right-hand motor imagery tasks,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 1, pp. 4–14, 2016. View at Publisher · View at Google Scholar · View at Scopus
  69. H. Yuan, T. Liu, R. Szarkowski, C. Rios, J. Ashe, and B. He, “Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: an EEG and fMRI study of motor imagery and movements,” NeuroImage, vol. 49, no. 3, pp. 2596–2606, 2010. View at Publisher · View at Google Scholar · View at Scopus
  70. B. Kamousi, A. N. Amini, and B. He, “Classification of motor imagery by means of cortical current density estimation and Von Neumann entropy,” Journal of Neural Engineering, vol. 4, no. 2, article no. 002, pp. 17–25, 2007. View at Publisher · View at Google Scholar · View at Scopus
  71. H. Ramoser, J. Müller-Gerking, and G. Pfurtscheller, “Optimal spatial filtering of single trial EEG during imagined hand movement,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 8, no. 4, pp. 441–446, 2000. View at Publisher · View at Google Scholar · View at Scopus
  72. G. R. Müller-Putz, I. Daly, and V. Kaiser, “Motor imagery-induced EEG patterns in individuals with spinal cord injury and their impact on brain-computer interface accuracy,” Journal of Neural Engineering, vol. 11, no. 3, Article ID 035011, 2014. View at Publisher · View at Google Scholar · View at Scopus
  73. G. Onose, C. Grozea, A. Anghelescu et al., “On the feasibility of using motor imagery EEG-based brain-computer interface in chronic tetraplegics for assistive robotic arm control: A clinical test and long-term post-trial follow-up,” Spinal Cord, vol. 50, no. 8, pp. 599–608, 2012. View at Publisher · View at Google Scholar · View at Scopus