The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Towards a Cure for BCI Illiteracy
Open accessAbstract
Brain-Computer Interfaces (BCIs) allow a user to control a computer application by brain activity as acquired, e.g., by EEG. One of the biggest challenges in BCI research is to understand and solve the problem of "BCI Illiteracy", which is that BCI control does not work for a non-negligible portion of users (estimated 15 to 30%). Here, we investigate the illiteracy problem in BCI systems which are based on the modulation of sensorimotor rhythms. In this paper, a sophisticated adaptation scheme is presented which guides the user from an initial subject-independent classifier that operates on simple features to a subject-optimized state-of-the-art classifier within one session while the user interacts the whole time with the same feedback application. While initial runs use supervised adaptation methods for robust co-adaptive learning of user and machine, final runs use unsupervised adaptation and therefore provide an unbiased measure of BCI performance. Using this approach, which does not involve any offline calibration measurement, good performance was obtained by good BCI participants (also one novice) after 3-6 min of adaptation. More importantly, the use of machine learning techniques allowed users who were unable to achieve successful feedback before to gain significant control over the BCI system. In particular, one participant had no peak of the sensory motor idle rhythm in the beginning of the experiment, but could develop such peak during the course of the session (and use voluntary modulation of its amplitude to control the feedback application).
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“and standard calibration pipelines fail to produce a functional classifier for a non-negligible portion of users [5].”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [5]
In the citing paper
“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 [25]
In the citing paper
“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 [25]
In the citing paper
“Firstly, works where users acquired BCI control (able-bodied [3,25] and end-users [40,41,51,62,63]) do not report any learning metric over time.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [25]
In the citing paper
“Some works find no evident learning effects at the neural correlate level [25,34].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [25]
In the citing paper
“Lack of robustness is a well-known issue of all BCI paradigms and has been associated to the nonstationarity of brain signals [18,25,27].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [25]
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Cites
Brain–computer interfaces for communication and control
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A well-conditioned estimator for large-dimensional covariance matrices
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Optimizing Spatial filters for Robust EEG Single-Trial Analysis
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K.-R. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.4408441 · Cited by 2,080 · cited 5×
“Its speed was determined by the classi-fication output (similar to Blankertz et al. (2007, 2008a)).”
BackgroundMethodInfluentialSelf-citationHoney, I Shrunk the Sample Covariance Matrix
Ledoit, O., & Wolf, M. (2004) · The Journal of Portfolio Management · doi:10.3905/jpm.2004.110 · Cited by 1,279
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
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Self-citationToward Brain-Computer Interfacing
Dornhege, G., Millán, J. D. R., Hinterberger, T., McFarland, D. J., & Müller, K.-R. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7493.001.0001 · Cited by 789
Covariate Shift Adaptation by Importance Weighted Cross Validation
Sugiyama, M., Krauledat, M., & Müller, K. (2007) · doi:10.5555/1314498.1390324 · Cited by 733
Dornhege, G., Blankertz, B., Curio, G., & Müller, K. R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827088 · Cited by 638
Self-citationBrain-computer communication: Unlocking the locked in.
Kübler, A., Kotchoubey, B., Kaiser, J., Wolpaw, J. R., & Birbaumer, N. (2001) · Psychological Bulletin · doi:10.1037/0033-2909.127.3.358 · Cited by 565
Research works printed but not linked: 1
- Vidaurre C, Schlögl A, Blankertz B, Kawanabe M, Müller KR (2008) Unsupervised adaptation of the LDA classifier for brain–computer interfaces. In: Proceedings of the 4th international brain–computer interface workshop and training course 2008, Verlag der Technischen Universität Graz, pp 122–127
Cited by
Sorted by citations
Single-trial analysis and classification of ERP components — A tutorial
Blankertz, B., Lemm, S., Treder, M., Haufe, S., & Müller, K.-R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.048 · Cited by 1,138
Self-citationCombining brain-computer interfaces and assistive technologies: state-of-the-art and challenges
Millán, J. D. R. (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00161 · Cited by 905
Towards the utilization of EEG as a brain imaging tool
Michel, C. M., & Murray, M. M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.039 · Cited by 774
Introduction to machine learning for brain imaging
Lemm, S., Blankertz, B., Dickhaus, T., & Müller, K. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.004 · Cited by 658
Self-citationEEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges
Padfield, N., Zabalza, J., Zhao, H., Masero, V., & Ren, J. (2019) · Sensors · doi:10.3390/s19061423 · Cited by 598
Metadata
- Authors
- Vidaurre, C., & Blankertz, B.
- Affiliations
- Technische Universität Berlin (DE)
- Corresponding author
- Vidaurre, Carmen
- Venue
- Brain Topography
- Published
- 2009-11-28
- Volume
- 23
- Issue
- 2
- Pages
- 194-198
- ISSN
- 0896-0267 (ISSN-L)
- 1573-6792
- PMC
- PMC2874052
- OpenAlex
- W2003621157
- Cited by
- 501
- References
- 24
- Citations by year
- 2026: 31 · 2025: 24 · 2024: 30 · 2023: 28 · 2022: 33 · 2021: 42 · 2020: 45 · 2019: 32 · 2018: 46 · 2017: 31 · 2016: 37 · 2015: 29 · 2014: 30 · 2013: 23 · 2012: 19
- FWCI
- 6.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Brain Topography (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, cc-by-nc)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- European Commission (224631, 216886)
- Deutsche Forschungsgemeinschaft (01GQ0850)
- Bundesministerium für Bildung und Forschung (01GQ0850, 01IB001A)
- 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 › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- BCI illiteracy, brain-computer interface, sensorimotor rhythm, EEG, online adaptation, unsupervised adaptation, supervised adaptation, novice users, motor imagery
- MeSH terms
- Algorithms, Artificial Intelligence, Brain, Electroencephalography, Humans, Imagination, Learning, Motor Activity, Signal Processing, Computer-Assisted, Time Factors, User-Computer Interface, Feedback, Psychological
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