Usability and Performance Measure of a Consumer-grade Brain Computer Interface System for Environmental Control by Neurological Patients

Authors

  • Farzin Deravi
  • Chee Siang Ang
  • M A Hannan Bin Azhar
  • Areej Al-Wabil
  • Malcolm Philips
  • Mohamed Sakel

Keywords:

stroke, rehabilitation, brain-computer interface, electroencephalography, emotiv EPOC

Abstract

With the increasing incidence and prevalence of chronic brain injury patients and the current financial constraints in healthcare budgets, there is a need for a more intelligent way to realise the current practice of neuro-rehabilitation service provision. Brain-computer Interface (BCI) systems have the potential to address this issue to a certain extent only if carefully designed research can demonstrate that these systems are accurate, safe, cost-effective, are able to increase patient/carer satisfaction and enhance their quality of life. Therefore, one of the objectives of the proposed study was to examine whether participants (patients with brain injury and a sample of reference population) were able to use a low cost BCI system (Emotiv EPOC) to interact with a computer and to communicate via spelling words. Patients participated in the study did not have prior experience in using BCI headsets so as to measure the user experience in the first-exposure to BCI training. To measure emotional arousal of participants we used an ElectroDermal Activity Sensor (Qsensor by Affectiva). For the signal processing and feature extraction of imagery controls the Cognitive Suite of Emotiv's Control Panel was used. Our study reports the key findings based on data obtained from a group of patients and a sample reference population and presents the implications for the design and development of a BCI system for communication and control. The study also evaluates the performance of the system when used practically in context of an acute clinical environment.

References

C. Ang, M. Sakel, M. Pepper, and M. Phillips, “Use of brain computer interface in neurological rehabilitation,” British Journal of Neuroscience Nursing, vol. 7, pp. 523-528, 2011

W. Wang, J. L. Collinger, M. A. Perez, E. C. Tyler-Kabara, L. G. Cohen, N. Birbaumer, et al., “Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity,” Physical Medicine and Rehabilitation Clinics of North America, vol. 21, no. 1, pp. 157-178, 2010.

N. Birbaumer and L. G. Cohen. “Brain-computer interfaces: communication and restoration of movement in paralysis,” J Physiol, vol. 579, no. 3, pp. 621-636, 2007.

R. Scherer, G. R. Müller-Putz and G. Pfurtscheller, “Flexibility and practicality graz brain-computer interface approach,” International Review of Neurobiology, vol. 86, pp. 119-131, 2009.

N. Birbaumer, A. Ramos-Murguialday, C. Weber and P. Montoya, “Neurofeedback and brain computer interface clinical applications,” International Review of Neurobiology, vol. 86, pp. 107-117, 2009.

B. Blankertz, F. Losch, M. Krauledat, G. Dornhege, G. Curio and KR. Müller, “The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 10, pp. 2452-2462, 2008.

N. Birbaumer, A. Kubler, N. Ghanayim, T. Hinterberger, J. Perelmouter, J. Kaiser, et al., “The thought translation device (TTD) for completely paralyzed patients,” IEEE Transactions on Rehabilitation Engineering, vol. 8, pp. 190-193, 2000.

G. Pfurtscheller, G. R. Müller, J. Pfurtscheller, H. J. Gerner and R. Rupp, “Thought'-control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia,” Neuroscience letters, vol. 351, pp. 33-36, 2003.

DJ. Krusienski and JR. Wolpaw, “Brain-computer interface research at the wadsworth center developments in noninvasive communication and control,” International Review of Neurobiology, vol. 86, pp. 147-157, 2009.

S. Weigelt, L. Muckli, and A. Kohler, “Functional magnetic resonance adaptation in visual neuroscience,” Reviews in the Neurosciences, vol. 19, no. 4-5, pp. 363-380, 2008.

S. Bermudez, A. M. Garcia , H. Samaha, and P. F. M. J. Verschure, “Using a hybrid brain computer interface and virtual reality system to monitor and promote cortical reorganization through motor activity and motor imagery training,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 2, pp. 174-181, 2013.

M. Gongora, C. Peressutti, S. Machado, S. Teixeira, B. Velasques and P. Ribeiro, “Progress and prospects in neurorehabilitation: clinical applications of stem cells and brain-computer interface for spinal cord lesions,” Neurological Sciences, vol. 34, no. 4, pp. 427-433, April 2013.

J. J. Daly and J. R. Wolpaw, “Brain-computer interfaces in neurological rehabilitation,” The Lancet Neurology, vol. 7, pp. 1032-1043, 2008.

N. Birbaumer, “Breaking the silence: brain-computer interfaces (BCI) for communication and motor control,” Psychophysiology, vol. 43, pp. 517-532, 2006.

J. M. Belda-Lois, S. Mena-Del Horno, I. Bermejo-Bosch, JC. Moreno, J. L. Pons, D. Farina, et al., “Rehabilitation of gait after stroke: a review towards a top-down approach,” Journal of NeuroEngineering and Rehabilitation, vol. 10, no. 1, 66 pages, 2011.

T. Isa, E. E. Fetz, and K. R. Müller, “Recent advances in brainmachine interfaces.” Neural Netw, vol. 22, pp. 1201-1202, 2009.

J. R. Millan, R. Rupp, G. Mueller-Putz, R. Murray-Smith, C. Giugliemma, M. Tanger-mann et al., “Combining braincomputer interfaces and assistive technologies: State-of-the art and challenges,” Front Neuro-Prosthetics, vol. 4, no. 161, September, 2010.

J. R.Wolpaw, “Brain-computer interfaces (ICCs) for communication and control: a mini-review,” Clinical Neurophysiology, vol. 57, pp. 607-613, 2004.

T. J. Sullivan, S. R. Deiss, T. P. Jung, and G. Cauwenberghs, “A brain-machine interface using dry-contact, low-noise EEG sensors,” IEEE International Symposium on Circuits and Systems, pp. 1986-1989, May 2008.

L. Liao, I. Wang, C. Chang, B. Lin, C. Lin and C. Kevin, “Human cognitive application by using wearable mobile brain computer interface,” TENCON, pp. 346-351, 2010.

L. Liao, C. Chen, I. Wang, S. Chen, S. Li, B. Chen, J. Chang, and C. Lin, “Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors,” Journal of Neuro Engineering and Rehabilitation, vol. 9, no.5, 2012.

G. Pfurtscheller, G. R. Muller-Putz, R. Scherer and C. Neuper, “Rehabilitation with brain-computer interface systems,” IEEE Computer , vol. 41, no. 10, pp.58-65, 2008.

Qsensor, http://qsensor-support.affectiva.com/, last visited in August 2015

Emotiv, https://emotiv.com/store/epoc-detail/, last visited in August 2015.

Techsmit, https://www.techsmith.com/morae-features.html, last visited in August 2015,

L. Seungchan, S. Younghak, W. Soogil, K. Kiseon and L. Heung-No, Review of wireless brain-computer interface systems, brain-computer interface systems - recent progress and future Prospects, Dr. Reza Fazel-Rezai (Ed.), 2013, ISBN: 978-953-51-1134-4, InTech.

A. Setare, F. R. Reza, and A. Vahid, “A review of hybrid brain-computer interface systems,” Advances in Human-Computer Interaction, vol. 2013, 8 pages, 2013.

N. S. Diasa, J. P. Carmo, P. M. Mendes, and J. H. Correiac, “Wireless instrumentation system based on dry electrodes for acquiring EEG signals,” Medical Engineering & Physics, vol. 34, pp. 972-981, 2012.

B. Z. Allison, C. Brunner, V. Kaiser, G. R. Müller-Putz, C. Neuper and G. Pfurtscheller, “Toward a hybrid brain-computer interface based on imagined movement and visual attention,” Journal of Neural Engineering, vol. 7, no. 2, March 2010.

P. Shenoy, M. Krauledat, B. Blankertz, R. P. N. Rao, and K. R. Müller. “Towards adaptive classification for BCI,” Journal of Neural Engineering, vol. 3, pp. 13-23, 2006.

C. Brunner, B. Z. Allison, C. Altstätter, and C. Neuper, “A comparison of three brain–computer interfaces based on event-related desynchronization, steady state visual evoked potentials, or a hybrid approach using both signals,” Journal of Neural Engineering, vol. 8, no. 2, 2011.

C. Vidaurre, A. Schlögl, R. Cabeza, R. Scherer, and G. Pfurtscheller, “Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 3, 2007.

System Usability Scale - http://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html, last visited in August 2015.

Downloads

Published

2015-07-01

How to Cite

[1]
F. Deravi, C. S. Ang, M. A. H. B. Azhar, A. Al-Wabil, M. Philips, and M. Sakel, “Usability and Performance Measure of a Consumer-grade Brain Computer Interface System for Environmental Control by Neurological Patients”, Int. j. eng. technol. innov., vol. 5, no. 3, pp. 165–177, Jul. 2015.

Issue

Vol. 5 No. 3 (2015)

Section

Articles

License

Copyright Notice

 

Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright in their articles with no restrictions. Also, author can post the final, peer-reviewed manuscript version (postprint) to any repository or website.

 

 

Since Jan. 01, 2019, IJETI will publish new articles with Creative Commons Attribution Non-Commercial License, under Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License.

The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.