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BioMed Research International
Volume 2017 (2017), Article ID 9071568, 11 pages
https://doi.org/10.1155/2017/9071568
Clinical Study

An EEG Tool for Monitoring Patient Engagement during Stroke Rehabilitation: A Feasibility Study

1Rehabilitative Psychobiology Laboratory, Reuth Research and Development Institute, Reuth Rehabilitation Hospital, Tel Aviv, Israel
2Department of Rehabilitation, Reuth Rehabilitation Hospital, Tel Aviv, Israel
3Rehabilitation and Motor Control of Walking Laboratory, Department of Physiotherapy, Ben-Gurion University of the Negev, Beersheba, Israel
4Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
5BrainMARC Ltd., Yokneam, Israel

Correspondence should be addressed to Goded Shahaf

Received 8 June 2017; Accepted 13 August 2017; Published 24 September 2017

Academic Editor: Danilo S. Bocalini

Copyright © 2017 Gadi Bartur 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. A. H. Lequerica, C. S. Donnell, and D. G. Tate, “Patient engagement in rehabilitation therapy: Physical and occupational therapist impressions,” Disability and Rehabilitation, vol. 31, no. 9, pp. 753–760, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. O. Pyöriä, U. Talvitie, H. Nyrkkö, H. Kautiainen, T. Pohjolainen, and V. Kasper, “The effect of two physiotherapy approaches on physical and cognitive functions and independent coping at home in stroke rehabilitation. a preliminary follow-up study,” Disability and Rehabilitation, vol. 29, no. 6, pp. 503–511, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. M. M. Danzl, N. M. Etter, R. O. Andreatta, and P. H. Kitzman, “Facilitating neurorehabilitation through principles of engagement,” Journal of Allied Health, vol. 41, no. 1, pp. 35–41, 2012. View at Google Scholar · View at Scopus
  4. S. Paolucci, A. Di Vita, R. Massicci et al., “Impact of participation on rehabilitation Results: a multivariate study,” European Journal of Physical and Rehabilitation Medicine, vol. 48, no. 3, pp. 455–466, 2012. View at Google Scholar · View at Scopus
  5. F. A. S. Bright, N. M. Kayes, L. Worrall, and K. M. McPherson, “A conceptual review of engagement in healthcare and rehabilitation,” Disability and Rehabilitation, vol. 37, no. 8, pp. 643–654, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. R. Colombo, F. Pisano, A. Mazzone et al., “Design strategies to improve patient motivation during robot-aided rehabilitation,” Journal of NeuroEngineering and Rehabilitation, vol. 4, article 3, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Horton, A. Howell, K. Humby, and A. Ross, “Engagement and learning: an exploratory study of situated practice in multi-disciplinary stroke rehabilitation,” Disability and Rehabilitation, vol. 33, no. 3, pp. 270–279, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. A. A. Blank, J. A. French, A. U. Pehlivan, and M. K. O’Malley, “Current trends in robot-assisted upper-limb stroke rehabilitation: promoting patient engagement in therapy,” Current Physical Medicine and Rehabilitation Reports, vol. 2, no. 3, pp. 184–195, 2014. View at Publisher · View at Google Scholar
  9. A. H. Lequerica, L. J. Rapport, R. D. Whitman et al., “Psychometric properties of the rehabilitation therapy engagement scale when used among individuals with acquired brain injury,” Rehabilitation Psychology, vol. 51, no. 4, pp. 331–337, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. C. E. Brett, C. Sykes, and R. Pires-Yfantouda, “Interventions to increase engagement with rehabilitation in adults with acquired brain injury: a systematic review,” Neuropsychological Rehabilitation, pp. 959-–982, 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Conte, F. Gilio, E. Iezzi, V. Frasca, M. Inghilleri, and A. Berardelli, “Attention influences the excitability of cortical motor areas in healthy humans,” Experimental Brain Research, vol. 182, no. 1, pp. 109–117, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Jueptner, K. M. Stephan, C. D. Frith, D. J. Brooks, R. S. J. Frackowiak, and R. E. Passingham, “Anatomy of motor learning. I. Frontal cortex and attention to action,” Journal of Neurophysiology, vol. 77, no. 3, pp. 1313–1324, 1997. View at Google Scholar · View at Scopus
  13. K. Stefan, M. Wycislo, and J. Classen, “Modulation of associative human motor cortical plasticity by attention,” Journal of Neurophysiology, vol. 92, no. 1, pp. 66–72, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. X. L. Hu, K.-Y. Tong, R. Song, X. J. Zheng, and W. W. F. Leung, “A comparison between electromyography-driven robot and passive motion device on wrist rehabilitation for chronic stroke,” Neurorehabilitation and Neural Repair, vol. 23, no. 8, pp. 837–846, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Shahaf, A. Reches, N. Pinchuk et al., “Introducing a novel approach of network oriented analysis of ERPs, demonstrated on adult attention deficit hyperactivity disorder,” Clinical Neurophysiology, vol. 123, no. 8, pp. 1568–1580, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. W. N. Park, G. H. Kwon, D.-H. Kim, Y.-H. Kim, S.-P. Kim, and L. Kim, “Assessment of cognitive engagement in stroke patients from single-trial EEG during motor rehabilitation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 3, pp. 351–362, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. G. Tacchino, M. Gandolla, S. Coelli, R. Barbieri, A. Pedrocchi, and A. M. Bianchi, “EEG Analysis During Active and Assisted Repetitive Movements: Evidence for Differences in Neural Engagement,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 6, pp. 761–771, 2017. View at Publisher · View at Google Scholar
  18. A. M. Goldfine and N. D. Schiff, “What is the role of brain mechanisms underlying arousal in recovery of motor function after structural brain injuries?” Current Opinion in Neurology, vol. 24, no. 6, pp. 564–569, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. G. Shahaf, T. Fisher, J. Aharon-Peretz, and H. Pratt, “Comprehensive analysis suggests simple processes underlying EEG/ERP - Demonstration with the go/no-go paradigm in ADHD,” Journal of Neuroscience Methods, vol. 239, pp. 183–193, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. G. Shahaf and H. Pratt, “Thorough specification of the neurophysiologic processes underlying behavior and of their manifestation in EEG - Demonstration with the go/no-go task,” Frontiers in Human Neuroscience, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Vijayalakshmi and A. M. Abhishek, “Spike Detection in Epileptic Patients EEG Data using Template Matching Technique,” International Journal of Computer Applications, vol. 2, no. 6, pp. 5–8, 2010. View at Publisher · View at Google Scholar
  22. P. Jaskowski and R. Verleger, “Amplitudes and latencies of single-trial ERP's estimated by a maximum-likelihood method,” IEEE Transactions on Biomedical Engineering, vol. 46, no. 8, pp. 987–993, 1999. View at Publisher · View at Google Scholar · View at Scopus
  23. E. L. Altschuler, S. B. Wisdom, L. Stone et al., “Rehabilitation of hemiparesis after stroke with a mirror,” The Lancet, vol. 353, no. 9169, pp. 2035-2036, 1999. View at Publisher · View at Google Scholar · View at Scopus
  24. F. Peterson-Kendall, E. Kendall-McCreary, P. Geise-Provance, M. McIntyre-Rodgers, and W. A. Romani, Muscles Testing And Function with Posture and Pain, 2005.
  25. G. Rebolledo-Mendez, I. Dunwell, E. A. Martínez-Mirón et al., “Assessing neurosky's usability to detect attention levels in an assessment exercise,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5610, no. 1, pp. 149–158, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. S. K. Meehan, B. Randhawa, B. Wessel, and L. A. Boyd, “Implicit sequence-specific motor learning after subcortical stroke is associated with increased prefrontal brain activations: an fMRI Study,” Human Brain Mapping, vol. 32, no. 2, pp. 290–303, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. E. Carmeli, S. Peleg, G. Bartur, E. Elbo, and J.-J. Vatine, “HandTutorTM enhanced hand rehabilitation after stroke - a pilot study,” Physiotherapy Research International, vol. 16, no. 4, pp. 191–200, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. M. J. Fu, J. S. Knutson, and J. Chae, “Stroke rehabilitation using virtual environments,” Physical Medicine and Rehabilitation Clinics of North America, vol. 26, no. 4, pp. 747–757, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. J. C. Perry, S. Balasubramanian, C. Rodriguez-De-Pablo, and T. Keller, “Improving the match between ability and challenge: Toward a framework for automatic level adaptation in game-based assessment and training,” in Proceedings of the 2013 IEEE 13th International Conference on Rehabilitation Robotics, ICORR 2013, Seattle, Wash, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. C. James, L. MacKenzie, and M. Capra, “Inter-and intra-rater reliability of the manual handling component of the WorkHab Functional Capacity Evaluation,” Disability and Rehabilitation, vol. 33, no. 19-20, pp. 1797–1804, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. C. J. Winstein, S. L. Wolf, A. W. Dromerick et al., “Interdisciplinary comprehensive arm rehabilitation evaluation (ICARE): a randomized controlled trial protocol,” BMC Neurology, vol. 13, article 5, 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. D. B. Shahaf, G. Shahaf, J. Mehta, and L. Venkatraghavan, “Intracarotid etomidate decreases the interhemispheric synchronization in electroencephalogram (EEG) during the wada test,” Journal of Neurosurgical Anesthesiology, vol. 28, no. 4, pp. 341–346, 2016. View at Publisher · View at Google Scholar · View at Scopus
  33. D. Yang and J. E. Dalton, “A unified approach to measuring the effect size between two groups using SAS®,” In SAS Global Forum, vol. 335, pp. 1–6, 2012. View at Google Scholar
  34. B. Reimer and B. Mehler, “The impact of cognitive workload on physiological arousal in young adult drivers: A field study and simulation validation,” Ergonomics, vol. 54, no. 10, pp. 932–942, 2011. View at Publisher · View at Google Scholar · View at Scopus
  35. M. R. Sutherland, D. A. McQuiggan, J. D. Ryan, and M. Mather, “Perceptual salience does not influence emotional arousal's impairing effects on top-down attention,” Emotion, vol. 17, no. 4, pp. 700–706, 2017. View at Publisher · View at Google Scholar · View at Scopus
  36. R.-S. Huang, T.-P. Jung, and S. Makeig, “Event-related brain dynamics in continuous sustained-attention tasks,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4565, pp. 65–74, 2007. View at Google Scholar · View at Scopus
  37. P. Masterson-Algar, C. R. Burton, and J. Rycroft-Malone, “Process evaluations in neurological rehabilitation: A mixed-evidence systematic review and recommendations for future research,” BMJ Open, vol. 6, no. 11, Article ID e013002, 2016. View at Publisher · View at Google Scholar · View at Scopus