Advertisement

Towards Brain Computer Interfaces for Recreational Activities: Piloting a Drone

  • Nataliya Kosmyna
  • Franck Tarpin-Bernard
  • Bertrand Rivet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9296)

Abstract

Active Brain Computer Interfaces (BCIs) allow people to exert voluntary control over a computer system: brain signals are captured and imagined actions (movements, concepts) are recognized after a training phase (from 10 min to 2 months). BCIs are confined in labs, with only a few dozen people using them outside regularly (e.g. assistance for impairments). We propose a “Co-learning BCI” (CLBCI) that reduces the amount of training and makes BCIs more suitable for recreational applications. We replicate an existing experiment where the BCI controls a drone and compare CLBCI to their Operant Conditioning (OC) protocol over three durations of practice (1 day, 1 week, 1 month). We find that OC works at 80 % after a month practice, but the performance is between 60 and 70 % any earlier. In a week of practice, CLBCI reaches a performance of around 75 %. We conclude that CLBCI is better suited for recreational use. OC should be reserved for users for whom performance is the main concern.

Keywords

Brain computer interface Engagement Replication Drone 

References

  1. 1.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002)CrossRefGoogle Scholar
  2. 2.
    Future BNCI: A Roadmap for Future Directions in Brain/Neuronal Computer Interaction Research. Future BNCI Program under the European Union Seventh Framework Programme, FP7/2007–2013, grant 248320 (2012). http://future-bnci.org/images/stories/Future_BNCI_Roadmap.pdf
  3. 3.
    Mancini, C., Rogers, Y., Bandara, A.K., Coe, T., Jedrzejczyk, L., Joinson, A.N., Price, B.A., Thomas, K., Nuseibeh, B.: Contravision: exploring users’ reactions to futuristic technology. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 153–162. ACM, New York (2010)Google Scholar
  4. 4.
    LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., He, B.: Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface. J. Neural Eng. 10, 046003 (2013)CrossRefGoogle Scholar
  5. 5.
    Blankertz, B., Losch, F., Krauledat, M., Dornhege, G., Curio, G., Müller, K.R.: The Berlin brain - computer interface: Accurate performance from first-session in BCI-naïve subjects. IEEE Trans. Biomed. Eng. 55, 2452–2462 (2008)CrossRefGoogle Scholar
  6. 6.
    Lotte, F., Larrue, F., Mühl, C.: Flaws in current human training protocols for spontaneous brain-computer interfaces: lessons learned from instructional design. Front. Hum. Neurosci. 7, 568 (2013)CrossRefGoogle Scholar
  7. 7.
    Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain computer interfaces, a review. Sensors 12, 1211–1279 (2012)CrossRefGoogle Scholar
  8. 8.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4, R1–R13 (2007)CrossRefGoogle Scholar
  9. 9.
    Nooh, A., Yunus, J., Daud, S.: A review of asynchronous electroencephalogram-based brain computer interface systems. Int. Conf. Biomed. Eng. Technol. 11, 55–59 (2011)Google Scholar
  10. 10.
    Nijholt, A., Reuderink, B., Oude Bos, D.: Turning shortcomings into challenges: brain-computer interfaces for games. In: Nijholt, A., Reidsma, D., Hondorp, H. (eds.) INTETAIN 2009. LNICST, vol. 9, pp. 153–168. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Kosmyna, N., Tarpin-Bernard, F.: Evaluation and comparison of a multimodal combination of BCI paradigms and eye tracking with affordable consumer-grade hardware in a gaming context. IEEE Trans. Comput. Intell. AI Games 5, 150–154 (2013)CrossRefGoogle Scholar
  12. 12.
    Kos’myna, N., Tarpin-Bernard, F., Rivet, B.: Towards a general architecture for a co-learning of brain computer interfaces. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 1054–1057. IEEE (2013)Google Scholar
  13. 13.
    Sung, Y., Cho, K., Um, K.: A development architecture for serious games using BCI (brain computer interface) sensors. Sensors (Basel) 12, 15671–15688 (2012)CrossRefzbMATHGoogle Scholar
  14. 14.
    Wang, Q., Sourina, O., Nguyen, M.K.: EEG-based “serious” games design for medical applications. In: 2010 International Conference on Cyberworlds, pp. 270–276 (2010)Google Scholar
  15. 15.
    Bell, C., Shenoy, P., Chalodhorn, R., Rao, C.: Control of a humanoid robot by a noninvasive brain-computer interface in humans. J. Neural Eng. 5, 214–220 (2008)CrossRefGoogle Scholar
  16. 16.
    Hochberg, L.R., Serruya, M.D., Friehs, G.M., Mukand, J.A., Saleh, M., Caplan, A.H., Branner, A., Chen, D., Penn, R.D., Donoghue, J.P.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006)CrossRefGoogle Scholar
  17. 17.
    Lécuyer, A., Lotte, F., Reilly, R., Leeb, R.: Brain-computer interfaces, virtual reality, and videogames. Comput. (Long. Beach. Calif) 42, 66–72 (2008)Google Scholar
  18. 18.
    Guger, C., Holzner, C., Groenegress, C.: Brain computer interface for virtual reality control. In: Proceedings of the 7th European Symposium on Artificial Neural Networks, pp. 443–448 (2009)Google Scholar
  19. 19.
    Royer, A., Doud, A., Rose, M., He, B.: EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 581–589 (2010)CrossRefGoogle Scholar
  20. 20.
    Doud, A., Lucas, J., Pisansky, M., He, B.: Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface. PLoS One 6, e26322 (2011)CrossRefGoogle Scholar
  21. 21.
    Kosmyna, N., Tarpin-Bernard, F., Rivet, B.: Bidirectional feedback in motor imagery bcis: learn to control a drone within 5 min. In: CHI 2014 Extended Abstracts on Human Factors in Computing Systems, pp. 479–482. ACM, New York (2014)Google Scholar
  22. 22.
    Kosmyna, N., Tarpin-Bernard, F., Rivet, B.: Drone,your brain, ring course – accept the challenge and prevail! In: UBICOMP 2014 ADJUNCT, pp. 243–246. ACM, New York (2014)Google Scholar
  23. 23.
    Scherer, R., Lee, F., Schlogl, A., Leeb, R., Bischof, H., Pfurtscheller, G.: Toward self-paced brain-computer communication: navigation through virtual worlds. IEEE Trans. Biomed. Eng. 55, 675–682 (2008)CrossRefGoogle Scholar
  24. 24.
    Faller, J., Scherer, R., Costa, U., Opisso, E., Medina, J., Müller-Putz, G.R.: A co-adaptive brain-computer interface for end users with severe motor impairment. PLoS ONE 9, e101168 (2014)CrossRefGoogle Scholar
  25. 25.
    Faller, J., Vidaurre, C., Solis-Escalante, T., Neuper, C., Scherer, R.: Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 313–319 (2012)CrossRefGoogle Scholar
  26. 26.
    Fails, J.A., Olsen Jr., D.R.: Interactive machine learning. In: Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 39–45. ACM, New York (2003)Google Scholar
  27. 27.
    Duda, R., Hart, P., Stok, D.: Pattern Recognition, 2nd edn. Wiley-Interscience, Hoboken (2001)Google Scholar
  28. 28.
    Barachant, A., Bonnet, S., Congedo, M., Jutten, C.: Multiclass brain-computer interface classification by Riemannian geometry. IEEE Trans. Biomed. Eng. 59, 920–928 (2012)CrossRefGoogle Scholar
  29. 29.
    Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Comput. 9, 1483–1492 (1997)CrossRefGoogle Scholar
  30. 30.
    Kosmyna, N., Tarpin-Bernard, F., Rivet, B.: Adding human learning in brain computer interfaces (BCIs): towards a practical control modality. ACM Trans. Comput. Interact. ACM SIGCHI (2015, to appear)Google Scholar
  31. 31.
    Müller-Putz, G.R., Scherer, R.: Better than random? A closer look on BCI results. Int. J. Bioelectromagn. 10, 52–55 (2008)Google Scholar
  32. 32.
    Congedo, M., Goyat, M., Tarrin, N., Varnet., L., Rivet, B., Ionescu, G., Jrad, N., Phlypo, R., Acquadro, M., Jutten, C.: “Brain Invaders”: a prototype of an open-source P300-based video game working with the OpenViBE platform. 5th International BCI Conference, Graz, Austria, 280–283 (2011) Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Nataliya Kosmyna
    • 1
  • Franck Tarpin-Bernard
    • 1
    • 2
  • Bertrand Rivet
    • 3
  1. 1.LIG-IIHMGrenoble Cedex 9France
  2. 2.Scientific Brain TrainingVilleurbanne CedexFrance
  3. 3.Gipsa-Lab/Grenoble INPSaint Martin d’HèresFrance

Personalised recommendations