Skip to main content
Top
Published in: Journal of NeuroEngineering and Rehabilitation 1/2017

Open Access 01-12-2017 | Research

Cybathlon experiences of the Graz BCI racing team Mirage91 in the brain-computer interface discipline

Authors: Karina Statthaler, Andreas Schwarz, David Steyrl, Reinmar Kobler, Maria Katharina Höller, Julia Brandstetter, Lea Hehenberger, Marvin Bigga, Gernot Müller-Putz

Published in: Journal of NeuroEngineering and Rehabilitation | Issue 1/2017

Login to get access

Abstract

Background

In this work, we share our experiences made at the world-wide first CYBATHLON, an event organized by the Eidgenössische Technische Hochschule Zürich (ETH Zürich), which took place in Zurich in October 2016. It is a championship for severely motor impaired people using assistive prototype devices to compete against each other. Our team, the Graz BCI Racing Team MIRAGE91 from Graz University of Technology, participated in the discipline “Brain-Computer Interface Race”. A brain-computer interface (BCI) is a device facilitating control of applications via the user’s thoughts. Prominent applications include assistive technology such as wheelchairs, neuroprostheses or communication devices. In the CYBATHLON BCI Race, pilots compete in a BCI-controlled computer game.

Methods

We report on setting up our team, the BCI customization to our pilot including long term training and the final BCI system. Furthermore, we describe CYBATHLON participation and analyze our CYBATHLON result.

Results

We found that our pilot was compliant over the whole time and that we could significantly reduce the average runtime between start and finish from initially 178 s to 143 s. After the release of the final championship specifications with shorter track length, the average runtime converged to 120 s. We successfully participated in the qualification race at CYBATHLON 2016, but performed notably worse than during training, with a runtime of 196 s.

Discussion

We speculate that shifts in the features, due to the nonstationarities in the electroencephalogram (EEG), but also arousal are possible reasons for the unexpected result. Potential counteracting measures are discussed.

Conclusions

The CYBATHLON 2016 was a great opportunity for our student team. We consolidated our theoretical knowledge and turned it into practice, allowing our pilot to play a computer game. However, further research is required to make BCI technology invariant to non-task related changes of the EEG.
Literature
2.
go back to reference R. Riener, “The Cybathlon promotes the development of assistive technology for people with physical disabilities,” J. Neuroeng. Rehabil., vol. 13, no. 1, p. 49, May 2016. R. Riener, “The Cybathlon promotes the development of assistive technology for people with physical disabilities,” J. Neuroeng. Rehabil., vol. 13, no. 1, p. 49, May 2016.
5.
go back to reference J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin Neurophysiol, vol. 113, no. 6, pp. 767–791, Jun. 2002. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin Neurophysiol, vol. 113, no. 6, pp. 767–791, Jun. 2002.
6.
go back to reference Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proc IEEE. 2001;89(7):1123–34.CrossRef Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proc IEEE. 2001;89(7):1123–34.CrossRef
7.
go back to reference Neuper C, Scherer R, Reiner M, Pfurtscheller G. Imagery of motor actions: differential effects of kinesthetic and visual–motor mode of imagery in single-trial EEG. Cogn Brain Res. 2005;25(3):668–77.CrossRef Neuper C, Scherer R, Reiner M, Pfurtscheller G. Imagery of motor actions: differential effects of kinesthetic and visual–motor mode of imagery in single-trial EEG. Cogn Brain Res. 2005;25(3):668–77.CrossRef
8.
go back to reference S. Halder et al., “Brain-controlled applications using dynamic P300 speller matrices,” Artif Intell Med, vol. 63, no. 1, pp. 7–17, Jan. 2015. S. Halder et al., “Brain-controlled applications using dynamic P300 speller matrices,” Artif Intell Med, vol. 63, no. 1, pp. 7–17, Jan. 2015.
9.
go back to reference C. Zickler, S. Halder, S. C. Kleih, C. Herbert, and A. Kübler, “Brain painting: usability testing according to the user-centered design in end users with severe motor paralysis,” Artif Intell Med, vol. 59, no. 2, pp. 99–110, Oct. 2013. C. Zickler, S. Halder, S. C. Kleih, C. Herbert, and A. Kübler, “Brain painting: usability testing according to the user-centered design in end users with severe motor paralysis,” Artif Intell Med, vol. 59, no. 2, pp. 99–110, Oct. 2013.
10.
go back to reference A. Pinegger, H. Hiebel, S. C. Wriessnegger, and G. R. Müller-Putz, “Composing only by thought: Novel application of the P300 brain-computer interface,” PLoS One, vol. 12, no. 9, p. e0181584, Sep. 2017. A. Pinegger, H. Hiebel, S. C. Wriessnegger, and G. R. Müller-Putz, “Composing only by thought: Novel application of the P300 brain-computer interface,” PLoS One, vol. 12, no. 9, p. e0181584, Sep. 2017.
11.
go back to reference A. Schwarz, P. Ofner, J. Pereira, A. I. Sburlea, and G. R. Müller-Putz, “Decoding natural reach-and-grasp actions from human EEG,” J Neural Eng, Aug. 2017. A. Schwarz, P. Ofner, J. Pereira, A. I. Sburlea, and G. R. Müller-Putz, “Decoding natural reach-and-grasp actions from human EEG,” J Neural Eng, Aug. 2017.
12.
go back to reference P. Ofner, A. Schwarz, J. Pereira, and G. R. Müller-Putz, “Upper limb movements can be decoded from the time-domain of low-frequency EEG,” PLoS One, vol. 12, no. 8, p. e0182578, Aug. 2017. P. Ofner, A. Schwarz, J. Pereira, and G. R. Müller-Putz, “Upper limb movements can be decoded from the time-domain of low-frequency EEG,” PLoS One, vol. 12, no. 8, p. e0182578, Aug. 2017.
13.
go back to reference Rohm M, et al. Hybrid brain–computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury. Artif Intell Med. 2013;59(2):133–42.CrossRefPubMed Rohm M, et al. Hybrid brain–computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury. Artif Intell Med. 2013;59(2):133–42.CrossRefPubMed
14.
go back to reference G. R. Müller-Putz et al., “MoreGrasp: Restoration of upper limb function in individuals with spinal cord injury by multimodal neuroprostheses for interaction in daily life activities,” in Proceedings of the 7th Graz Brain-Computer Interface Conference, Graz, Austria, 09/2017, pp. 338–343. G. R. Müller-Putz et al., “MoreGrasp: Restoration of upper limb function in individuals with spinal cord injury by multimodal neuroprostheses for interaction in daily life activities,” in Proceedings of the 7th Graz Brain-Computer Interface Conference, Graz, Austria, 09/2017, pp. 338–343.
15.
go back to reference Leeb R, Friedman D, Müller-Putz GR, Scherer R, Slater M, Pfurtscheller G. Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput Intell Neurosci. 2007;2007:1–8.CrossRef Leeb R, Friedman D, Müller-Putz GR, Scherer R, Slater M, Pfurtscheller G. Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput Intell Neurosci. 2007;2007:1–8.CrossRef
16.
go back to reference Herweg A, Gutzeit J, Kleih S, Kübler A. Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation. Biol Psychol. 2016;121:117–24.CrossRefPubMed Herweg A, Gutzeit J, Kleih S, Kübler A. Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation. Biol Psychol. 2016;121:117–24.CrossRefPubMed
17.
go back to reference Muller-Putz G, et al. Towards Noninvasive Hybrid Brain–Computer Interfaces: Framework, Practice, Clinical Application, and Beyond. Proc IEEE. 2015;103(6):926–43.CrossRef Muller-Putz G, et al. Towards Noninvasive Hybrid Brain–Computer Interfaces: Framework, Practice, Clinical Application, and Beyond. Proc IEEE. 2015;103(6):926–43.CrossRef
18.
go back to reference Rupp R, Rohm M, Schneiders M, Kreilinger A, Muller-Putz GR. Functional rehabilitation of the paralyzed upper extremity after spinal cord injury by noninvasive hybrid Neuroprostheses. Proc IEEE. 2015;103(6):954–68.CrossRef Rupp R, Rohm M, Schneiders M, Kreilinger A, Muller-Putz GR. Functional rehabilitation of the paralyzed upper extremity after spinal cord injury by noninvasive hybrid Neuroprostheses. Proc IEEE. 2015;103(6):954–68.CrossRef
19.
go back to reference J. Faller, C. Vidaurre, T. Solis-Escalante, C. Neuper, and R. Scherer, “Autocalibration and recurrent adaptation: towards a plug and play online ERD-BCI,” IEEE Trans Neural Syst Rehabil Eng, vol. 20, no. 3, pp. 313–319, May 2012. J. Faller, C. Vidaurre, T. Solis-Escalante, C. Neuper, and R. Scherer, “Autocalibration and recurrent adaptation: towards a plug and play online ERD-BCI,” IEEE Trans Neural Syst Rehabil Eng, vol. 20, no. 3, pp. 313–319, May 2012.
20.
go back to reference A. Kübler et al., “The user-centered design as novel perspective for evaluating the usability of BCI-controlled applications,” PLoS One, vol. 9, no. 12, p. e112392, Dec. 2014. A. Kübler et al., “The user-centered design as novel perspective for evaluating the usability of BCI-controlled applications,” PLoS One, vol. 9, no. 12, p. e112392, Dec. 2014.
21.
go back to reference M. Schreuder et al., “User-centered design in brain-computer interfaces-a case study,” Artif Intell Med, vol. 59, no. 2, pp. 71–80, Oct. 2013. M. Schreuder et al., “User-centered design in brain-computer interfaces-a case study,” Artif Intell Med, vol. 59, no. 2, pp. 71–80, Oct. 2013.
22.
go back to reference A. Kübler, G. Müller-Putz, and D. Mattia, “User-centred design in brain-computer interface research and development,” Ann Phys Rehabil Med, vol. 58, no. 5, pp. 312–314, Oct. 2015. A. Kübler, G. Müller-Putz, and D. Mattia, “User-centred design in brain-computer interface research and development,” Ann Phys Rehabil Med, vol. 58, no. 5, pp. 312–314, Oct. 2015.
23.
go back to reference G. R. Müller-Putz, R. Scherer, G. Pfurtscheller, and R. Rupp, “EEG-based neuroprosthesis control: a step towards clinical practice,” Neurosci Lett, vol. 382, no. 1–2, pp. 169–174, Apr. 2005. G. R. Müller-Putz, R. Scherer, G. Pfurtscheller, and R. Rupp, “EEG-based neuroprosthesis control: a step towards clinical practice,” Neurosci Lett, vol. 382, no. 1–2, pp. 169–174, Apr. 2005.
24.
go back to reference G. R. Müller-Putz, R. Scherer, G. Pfurtscheller, and C. Neuper, “Temporal coding of brain patterns for direct limb control in humans,” Front Neurosci, vol. 4, Jun. 2010. G. R. Müller-Putz, R. Scherer, G. Pfurtscheller, and C. Neuper, “Temporal coding of brain patterns for direct limb control in humans,” Front Neurosci, vol. 4, Jun. 2010.
25.
go back to reference E. V. C. Friedrich, C. Neuper, and R. Scherer, “Whatever works: a systematic user-centered training protocol to optimize brain-computer interfacing individually,” PLoS One, vol. 8, no. 9, p. e76214, Sep. 2013. E. V. C. Friedrich, C. Neuper, and R. Scherer, “Whatever works: a systematic user-centered training protocol to optimize brain-computer interfacing individually,” PLoS One, vol. 8, no. 9, p. e76214, Sep. 2013.
26.
go back to reference R. Scherer et al., “Individually adapted imagery improves brain-computer interface performance in end-users with disability,” PLoS One, vol. 10, no. 5, p. e0123727, May 2015. R. Scherer et al., “Individually adapted imagery improves brain-computer interface performance in end-users with disability,” PLoS One, vol. 10, no. 5, p. e0123727, May 2015.
27.
go back to reference Schwarz A, Steyrl D, Muller-Putz GR. Brain-computer interface adaptation for an end user to compete in the Cybathlon. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2016. Schwarz A, Steyrl D, Muller-Putz GR. Brain-computer interface adaptation for an end user to compete in the Cybathlon. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2016.
28.
go back to reference Schwarz A, Scherer R, Steyrl D, Faller J, Muller-Putz GR. A co-adaptive sensory motor rhythms brain-computer Interface based on common spatial patterns and random Forest. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2015. Schwarz A, Scherer R, Steyrl D, Faller J, Muller-Putz GR. A co-adaptive sensory motor rhythms brain-computer Interface based on common spatial patterns and random Forest. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2015.
29.
go back to reference Steyrl D, Scherer R, Faller J, Müller-Putz GR. Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier. Biomed. Tech. 2016;61(1) Steyrl D, Scherer R, Faller J, Müller-Putz GR. Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier. Biomed. Tech. 2016;61(1)
30.
go back to reference B. Hjorth, “An on-line transformation of EEG scalp potentials into orthogonal source derivations,” Electroencephalogr Clin Neurophysiol, vol. 39, no. 5, pp. 526–530, Nov. 1975. B. Hjorth, “An on-line transformation of EEG scalp potentials into orthogonal source derivations,” Electroencephalogr Clin Neurophysiol, vol. 39, no. 5, pp. 526–530, Nov. 1975.
31.
go back to reference B. Graimann, J. E. Huggins, S. P. Levine, and G. Pfurtscheller, “Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data,” Clin Neurophysiol, vol. 113, no. 1, pp. 43–47, Jan. 2002. B. Graimann, J. E. Huggins, S. P. Levine, and G. Pfurtscheller, “Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data,” Clin Neurophysiol, vol. 113, no. 1, pp. 43–47, Jan. 2002.
32.
go back to reference Ramoser H, Muller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng. 2000;8(4):441–6.CrossRefPubMed Ramoser H, Muller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng. 2000;8(4):441–6.CrossRefPubMed
33.
go back to reference K. K. Ang, Z. Y. Chin, C. Wang, C. Guan, and H. Zhang, “Filter Bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b,” Front Neurosci, vol. 6, p. 39, Mar. 2012. K. K. Ang, Z. Y. Chin, C. Wang, C. Guan, and H. Zhang, “Filter Bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b,” Front Neurosci, vol. 6, p. 39, Mar. 2012.
34.
go back to reference F. Lotte and C. Guan, “Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms,” IEEE Trans Biomed Eng, vol. 58, no. 2, pp. 355–362, Feb. 2011. F. Lotte and C. Guan, “Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms,” IEEE Trans Biomed Eng, vol. 58, no. 2, pp. 355–362, Feb. 2011.
35.
go back to reference Blankertz B, Lemm S, Treder M, Haufe S, Müller K-R. Single-trial analysis and classification of ERP components — a tutorial. NeuroImage. 2011;56(2):814–25.CrossRefPubMed Blankertz B, Lemm S, Treder M, Haufe S, Müller K-R. Single-trial analysis and classification of ERP components — a tutorial. NeuroImage. 2011;56(2):814–25.CrossRefPubMed
36.
go back to reference M. Billinger et al., “Is It Significant? Guidelines for Reporting BCI Performance,” in Biological and Medical Physics, Biomedical Engineering, 2012, pp. 333–354. M. Billinger et al., “Is It Significant? Guidelines for Reporting BCI Performance,” in Biological and Medical Physics, Biomedical Engineering, 2012, pp. 333–354.
37.
go back to reference W. Samek, C. Vidaurre, K.-R. Müller, and M. Kawanabe, “Stationary common spatial patterns for brain-computer interfacing,” J. Neural Eng., vol. 9, no. 2, p. 026013, Apr. 2012. W. Samek, C. Vidaurre, K.-R. Müller, and M. Kawanabe, “Stationary common spatial patterns for brain-computer interfacing,” J. Neural Eng., vol. 9, no. 2, p. 026013, Apr. 2012.
38.
go back to reference Scherer R, Schloegl A, Lee F, Bischof H, Jansa J, Pfurtscheller G. The self-paced graz brain-computer interface: methods and applications. Comput. Intell. Neurosci. 2007:79826. Scherer R, Schloegl A, Lee F, Bischof H, Jansa J, Pfurtscheller G. The self-paced graz brain-computer interface: methods and applications. Comput. Intell. Neurosci. 2007:79826.
40.
go back to reference L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” J. Mach. Learn. Res., vol. 9, no. Nov, pp. 2579–2605, Nov. 2008. L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” J. Mach. Learn. Res., vol. 9, no. Nov, pp. 2579–2605, Nov. 2008.
41.
go back to reference Grosse-Wentrup M, Schölkopf B. A Review of Performance Variations in SMR-Based Brain−Computer Interfaces (BCIs). SpringerBriefs in Electrical and Computer Engineering. 2013:39–51. Grosse-Wentrup M, Schölkopf B. A Review of Performance Variations in SMR-Based Brain−Computer Interfaces (BCIs). SpringerBriefs in Electrical and Computer Engineering. 2013:39–51.
42.
go back to reference P. Shenoy, M. Krauledat, B. Blankertz, R. P. N. Rao, and K.-R. Müller, “Towards adaptive classification for BCI,” J Neural Eng, vol. 3, no. 1, pp. R13–R23, Mar. 2006. P. Shenoy, M. Krauledat, B. Blankertz, R. P. N. Rao, and K.-R. Müller, “Towards adaptive classification for BCI,” J Neural Eng, vol. 3, no. 1, pp. R13–R23, Mar. 2006.
43.
go back to reference Leeb R, Lee F, Keinrath C, Scherer R, Bischof H, Pfurtscheller G. Brain–computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Trans. Neural Syst. Rehabil. Eng. 2007;15(4):473–82.CrossRefPubMed Leeb R, Lee F, Keinrath C, Scherer R, Bischof H, Pfurtscheller G. Brain–computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Trans. Neural Syst. Rehabil. Eng. 2007;15(4):473–82.CrossRefPubMed
44.
45.
go back to reference Kobler R, Scherer R. Restricted Boltzmann machines in sensory motor rhythm brain-computer interfacing: a study on inter-subject transfer and co-adaptation. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC): Budapest; 2016. Kobler R, Scherer R. Restricted Boltzmann machines in sensory motor rhythm brain-computer interfacing: a study on inter-subject transfer and co-adaptation. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC): Budapest; 2016.
46.
go back to reference Allison BZ, Dunne S, Leeb R, Del R Millán J, Nijholt A. Recent and Upcoming BCI Progress: Overview, Analysis, and Recommendations. In: Biological and Medical Physics, Biomedical Engineering; 2012. p. 1–13. Allison BZ, Dunne S, Leeb R, Del R Millán J, Nijholt A. Recent and Upcoming BCI Progress: Overview, Analysis, and Recommendations. In: Biological and Medical Physics, Biomedical Engineering; 2012. p. 1–13.
47.
go back to reference Grosse-Wentrup M, Schölkopf B. High gamma-power predicts performance in sensorimotor-rhythm brain–computer interfaces. J. Neural Eng. 2012;9(4):046001.CrossRefPubMed Grosse-Wentrup M, Schölkopf B. High gamma-power predicts performance in sensorimotor-rhythm brain–computer interfaces. J. Neural Eng. 2012;9(4):046001.CrossRefPubMed
48.
go back to reference Ahn M, Jun SC. Performance variation in motor imagery brain–computer interface: a brief review. J Neurosci Methods. 2015;243:103–10.CrossRefPubMed Ahn M, Jun SC. Performance variation in motor imagery brain–computer interface: a brief review. J Neurosci Methods. 2015;243:103–10.CrossRefPubMed
49.
go back to reference 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,” Front Neurosci, vol. 4, Jul. 2010. 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,” Front Neurosci, vol. 4, Jul. 2010.
50.
go back to reference M. Grosse-Wentrup, B. Schölkopf, and J. Hill, “Causal influence of gamma oscillations on the sensorimotor rhythm,” NeuroImage, vol. 56, no. 2, pp. 837–842, May 2011. M. Grosse-Wentrup, B. Schölkopf, and J. Hill, “Causal influence of gamma oscillations on the sensorimotor rhythm,” NeuroImage, vol. 56, no. 2, pp. 837–842, May 2011.
Metadata
Title
Cybathlon experiences of the Graz BCI racing team Mirage91 in the brain-computer interface discipline
Authors
Karina Statthaler
Andreas Schwarz
David Steyrl
Reinmar Kobler
Maria Katharina Höller
Julia Brandstetter
Lea Hehenberger
Marvin Bigga
Gernot Müller-Putz
Publication date
01-12-2017
Publisher
BioMed Central
Published in
Journal of NeuroEngineering and Rehabilitation / Issue 1/2017
Electronic ISSN: 1743-0003
DOI
https://doi.org/10.1186/s12984-017-0344-9

Other articles of this Issue 1/2017

Journal of NeuroEngineering and Rehabilitation 1/2017 Go to the issue