A Serious Game Application using EEG-based Brain Computer Interface

Francisco José Perales, Esperança Amengual

2013

Abstract

Serious games have demonstrated their effectiveness as a therapeutic resource to deal with motor, sensory and cognitive disabilities. In this article we consider Brain Computer Interfaces (BCI) as a new interaction mechanism that could be used in serious games to improve their rehabilitation activity thanks to the ability of neurofeedback to stimulate the cortical plasticity. We present the brief state-of-the-art of BCI serious games and the factors to be considered in order to develop this particular kind of software that could be highly complex and require experts with different knowledge and skills. We propose a new approach based on the detection of focus features in the game activity. We introduce a system able to assess the Alpha band variations in particular game tasks. Our initial target users are children with cerebral palsy and motor disa-bilities. The system is currently under evaluation with control users before to be operated with the target users in rehabilitation centers.

References

  1. Carrino, F., Dumoulin, J., Mugellini, E., Khaled, O., and Ingold, R. 2012. A self-paced bci system to control an electric wheel-chair: Evaluation of a commercial, lowcost eeg device. In Biosignalsand Biorobotics Conference (BRC), 2012 ISSNIP. IEEE, 1-6.
  2. Diaz, B., Sloot, L., Mansvelder, H., and LinkenkaerHansen, K. 2012. Eeg-biofeedback as a tool to modulate arousal: Trends and perspectives for treatment of ad hd and insomnia.
  3. Kaur, M., Ahmed, P., and Rafiq, M. 2012. Technology development for unblessed people using bci: A survey. International Journal of Computer Applications 40.
  4. Nijholt, A. 2009. Bci for games: a 'state of the art' survey. Entertainment Computing-ICEC 2008, 225-228.
  5. Rego, P., Moreira, P., and Reis, L. 2010. Serious games for rehabilitation: A survey and a classification towards a taxonomy. In Information Systems and Technologies (CISTI), 2010 5th Iberian Conference on. IEEE, 1-6.
  6. Sung Y., Cho, K., and Um, K. 2012. A development architecture for serious games using bci (brain computer interface) sensors. Sensors 12, 15671-15688.
  7. Sina 2009. SINA. Sistema de interacción natural avanzado. El ordenador al alcance de todos. 1-84 Editorial Eines. ISBN / ISBN: 978-84-613-1740-0. 2009. Dipòsit legal. PM-481-2009.
  8. Lun-De Liao, Chi-Yu Chen,I-Jan Wang, Sheng-Fu Chen, Shih-Yu Li , Bo-Wei Chen, Jyh-Yeong Chang, ChinTeng Lin. “Gaming Control Using a Wearable and Wireless EEG-Based Brain-Computer Interface Device with Novel Dry Foam-based Sensors, Journal of NeuroEngineering and Rehabilitation” 2012, 9:5. ISSN 1743-0003.
  9. C.-T. Lin, L.-W. Ko, J.-C. Chiou, J.-R. Duann, R.-S. Huang, T.-W. Chiu, S.-F. Liang and T.-P. Jung, "Noninvasive neural prostheses using mobile and wireless EEG," Proceedings of the IEEE, vol. 96, pp. 1167- 1183, 2008.
  10. C. T. Lin, I. F. Chung, L. W. Ko, Y. C. Chen, S. F. Liang and J. R. Duan, "EEG-based assessment of driver cognitive responses in a dynamic virtual-reality driving environment," IEEE Transactions on Biomedical Engineering, vol. 54, pp. 1349-1352, 2007.
  11. Hsieh S., Lin-Chao L., The nature of switch cost: task set configuration or carry-over effect?, Cognitive Brain Research 22, pp. 165-175, Elsevier,2005.
  12. A. F. Kramer. Physiological metrices of mental workload: A review of recent progress, In D. Damon (Ed.), Multiple Task Perfomance, pages 279-328, London, Taylor& Francis, 1991.
  13. W. Klimesch. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis, Brain Research Reviews, 29:169-195, Elsevier, 1999.
  14. Classifying mental states with machine learning algorithms using alpha activity decline. Carina Walter, Gabriele Cierniak, Peter Gerjets, Wolfgang Rosenstiel, Martin Bogdan. ANN 2011 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 27-29 April 2011, i6doc.com publ., ISBN 978-2-87419-044-5. Available from http:// www.i6doc.com/en/livre/?GCOI=28001100817300.
  15. BioEraPro visual designer for biofeedback. URL: http://www.bioera.net/. 2012.
  16. Johnny Chung Lee, and Desney S. Tan, “Using a low-cost electroencephalograph for task classification in HCI research”, Symposium on User Interface Software and Technology, Proceedings of the 19th annual ACM, Switzerland, Sensing from head to toe, 81 - 90, 2006.
  17. Romero, S. et al.: Filtrado ocular de señales EEG en el análisis de fármacos mediante topografía y tomografía cerebral. A: Simposio de Bioingenieía 2010. "Simposio de Bioningeniería 2010 (Redes REDINBIO y RETADIM)". 2010, p. 152-158.
Download


Paper Citation


in Harvard Style

Perales F. and Amengual E. (2013). A Serious Game Application using EEG-based Brain Computer Interface . In Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - Volume 1: BrainRehab, (NEUROTECHNIX 2013) ISBN 978-989-8565-80-8, pages 249-255. DOI: 10.5220/0004678102490255


in Bibtex Style

@conference{brainrehab13,
author={Francisco José Perales and Esperança Amengual},
title={A Serious Game Application using EEG-based Brain Computer Interface},
booktitle={Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - Volume 1: BrainRehab, (NEUROTECHNIX 2013)},
year={2013},
pages={249-255},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004678102490255},
isbn={978-989-8565-80-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - Volume 1: BrainRehab, (NEUROTECHNIX 2013)
TI - A Serious Game Application using EEG-based Brain Computer Interface
SN - 978-989-8565-80-8
AU - Perales F.
AU - Amengual E.
PY - 2013
SP - 249
EP - 255
DO - 10.5220/0004678102490255