A co-adaptive brain-computer interface for end users with severe motor impairment
Open accessAbstract
Co-adaptive training paradigms for event-related desynchronization (ERD) based brain-computer interfaces (BCI) have proven effective for healthy users. As of yet, it is not clear whether co-adaptive training paradigms can also benefit users with severe motor impairment. The primary goal of our paper was to evaluate a novel cue-guided, co-adaptive BCI training paradigm with severely impaired volunteers. The co-adaptive BCI supports a non-control state, which is an important step toward intuitive, self-paced control. A secondary aim was to have the same participants operate a specifically designed self-paced BCI training paradigm based on the auto-calibrated classifier. The co-adaptive BCI analyzed the electroencephalogram from three bipolar derivations (C3, Cz, and C4) online, while the 22 end users alternately performed right hand movement imagery (MI), left hand MI and relax with eyes open (non-control state). After less than five minutes, the BCI auto-calibrated and proceeded to provide visual feedback for the MI task that could be classified better against the non-control state. The BCI continued to regularly recalibrate. In every calibration step, the system performed trial-based outlier rejection and trained a linear discriminant analysis classifier based on one auto-selected logarithmic band-power feature. In 24 minutes of training, the co-adaptive BCI worked significantly (p = 0.01) better than chance for 18 of 22 end users. The self-paced BCI training paradigm worked significantly (p = 0.01) better than chance in 11 of 20 end users. The presented co-adaptive BCI complements existing approaches in that it supports a non-control state, requires very little setup time, requires no BCI expert and works online based on only two electrodes. The preliminary results from the self-paced BCI paradigm compare favorably to previous studies and the collected data will allow to further improve self-paced BCI systems for disabled users.
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Direct evidence of learned SMR modulation at the BCI feature level is rare or incomplete, derived in able-bodied populations and not longitudinal.
“Direct evidence of learned SMR modulation at the BCI feature level is, in fact, rare or incomplete, derived in able-bodied populations and not longitudinal [10,23,26,28,38–41].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [39]
Other studies have reported emergence of SMR modulations, but given the short number of experimental sessions, the observed neurophysiological patterns might only be indicative of transitory effects rather than consolidated subject learning.
“Other studies have reported emergence of such SMR modulations [26,28,38,39], but given the short number of experimental sessions they carried out, the observed neurophysiological patterns might only be indicative of transitory effects rather than consolidated subject learning.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [39]
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Jeevithashree, D. V., Jain, P. K., Mukhopadhyay, A., Saluja, K. P. S., & Biswas, P. (2021) · Technology and Disability · doi:10.3233/tad-200292 · Cited by 6 · see citing works
Evaluation of induced and evoked changes in EEG during selective attention to verbal stimuli
Horki, P., Bauernfeind, G., Schippinger, W., Pichler, G., & Muller-Putz, G. R. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.06.015 · Cited by 6 · see citing works
Kapgate, D. D. (2024) · Journal of Neurorestoratology · doi:10.1016/j.jnrt.2024.100109 · Cited by 4 · see citing works
Scherer, R., Faller, J., Opisso, E., Costa, U. M., Steyrl, D., & Muller-Putz, G. R. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318858 · Cited by 4 · see citing works
Self-citationAre Online Co-adaptive Sensorimotor Rhythm Brain-Computer Interface Training Paradigms Effective?
Cunha, J. D., & Scherer, R. (2018) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2018.00081 · Cited by 4 · see citing works
Self-citationSuwandjieff, P., Crell, M. R., Kostoglou, K., & Muller-Putz, G. R. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae6bef · Cited by 3 · see citing works
Muraleedharan, A., Anamika, J. H., Vishwakarma, H., Kashyap, K., & Biswas, P. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640544.3645236 · Cited by 2 · see citing works
Sultana, M., & Perdikis, S. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3456591 · Cited by 2 · see citing works
Auto-Adaptive Model for Longitudinal Motor Imagery Decoding in Amyotrophic Lateral Sclerosis
Patel, R., Bryson, J. B., Jiang, D., & Demosthenous, A. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10832078 · Cited by 2 · see citing works
Electroencephalography and Brain–Computer Interfaces
Muller-Putz, G. R., & Wriessnegger, S. C. (2021) · Springer eBooks · doi:10.1007/978-3-030-68545-4_3 · Cited by 2 · see citing works
Supervised autoencoder denoiser for non-stationarity in multi-session EEG-based BCI
Avin, O., Almagor, O., Yoav, N., Rosipal, R., & Shriki, O. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adc48e · Cited by 1 · see citing works
Kapgate, D. D., & Krishna Prasad, K. (2023) · Cognitive Computation and Systems · doi:10.1049/ccs2.12077 · Cited by 1 · see citing works
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Enabling Brain-Computer Interface Co-Adaptation with Performance-Related Signals
Wilson, N. R. (2019) · ResearchWorks at the University of Washington (University of Washington)
Enhancing Real-Time Online Motor Imagery Bci Performance: A Co-Adaptive Meta-Learning ApproachPreprint
Li, A., Wang, Z., Liu, H., Xu, T., Zhou, T., & Hu, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4719917
- Authors
- Faller, J., Scherer, R., Costa, U., Opisso, E., Medina, J., & Müller-Putz, G. R.
- Affiliations
- Graz University of Technology (AT)
- Universitat Autònoma de Barcelona (ES)
- Corresponding author
- Scherer, Reinhold
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-07-11
- Volume
- 9
- Issue
- 7
- Pages
- e101168
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4094431
- OpenAlex
- W1994043269
- Cited by
- 55
- References
- 51
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 7 · 2023: 2 · 2021: 6 · 2020: 3 · 2019: 5 · 2018: 7 · 2017: 2 · 2016: 14 · 2015: 6
- FWCI
- 4.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (247447)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- event-related desynchronization, severe motor impairment, self-calibration, outlier removal, linear discriminant analysis, motor imagery, assistive technology
- MeSH terms
- Adolescent, Adult, Aged, Electroencephalography, Female, Humans, Male, Middle Aged, Motor Activity, Movement Disorders, Young Adult, Brain-Computer Interfaces
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