Cybathlon experiences of the Graz BCI racing team Mirage91 in the brain-computer interface discipline
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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.
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Evidence that co-adaptive MI BCIs might also be able to promote and increase the ability of users to voluntarily modulate their brain signals (subject learning) is scarce, most often indirect and rather inconclusive.
“Evidence that co-adaptive MI BCIs might also be able to promote and increase the ability of the users to voluntary modulate their brain signals (subject learning) is, in fact, scarce, most often indirect and rather inconclusive.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [37]
Mutual learning has been claimed mostly on the grounds of adequate and improved BCI classification accuracy or application performances.
“Indeed, mutual learning has been claimed mostly on the grounds of adequate and improved BCI classification accuracy [25,27,30–35] or application performances [36,37].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [37]
Other training studies claim learnability in BCIs only on the grounds of improved online classification accuracy or application performances.
“Secondly, other training studies claim learnability in BCIs only on the grounds of improved online classification accuracy [9,27,30,31,33,35,51] or application performances [36,37].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [37]
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References 49 · Cited by 24
Research works printed but not linked: 1
- [14]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.
- Authors
- Statthaler, K., Schwarz, A., Steyrl, D., Kobler, R., Höller, M. K., Brandstetter, J., Hehenberger, L., Bigga, M., & Müller-Putz, G.
- Affiliations
- Graz University of Technology (AT)
- Venue
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2017-12
- Volume
- 14
- Issue
- 1
- Pages
- 129-129
- ISSN
- 1743-0003 (ISSN-L)
- PMC
- PMC5745791
- OpenAlex
- W2776198882
- Cited by
- 24
- References
- 49
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 4 · 2022: 2 · 2021: 8 · 2020: 4 · 2019: 2 · 2018: 2
- FWCI
- 1.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of NeuroEngineering and Rehabilitation (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- TUGraz OPEN Library (Graz University of Technology) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Horizon 2020 (643955)
- European Research Council (BE) (681231)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- Cybathlon, assistive technology, arousal, BCI training, neuroprosthetics, wheelchair control
- MeSH terms
- Disabled Persons, Persons with Disabilities, Humans, Male, Self-Help Devices, User-Computer Interface, Brain-Computer Interfaces
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