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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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“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]
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In the citing paper
“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
“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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Self-citationIrastorza-Landa, N., Sarasola-Sanz, A., Bibián, C., Ray, A. M., Insausti-Delgado, A., Helmhold, F., Mahmoud, W. J., Ortego-Isasa, I., López‐Larraz, E., & Ramos‐Murguialday, A. (2022) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_120-1 · Cited by 2
Subramanian, A., Shamsi, F., & Najafizadeh, L. (2023) · Springer eBooks · doi:10.1007/978-3-031-21236-9_3 · Cited by 2
Analysis of the Impact of the Presence of Physical Pain in fNIRS-based BCI Systems
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Decoding accuracies as well as ERP amplitudes do not show between-task correlations
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MEANSP: How Many Channels are Needed to Predict the Performance of a SMR-Based BCI?
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Self-citationEEG Characteristic Investigation of the Sixth-Finger Motor Imagery
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Quek, M. (2013) · Enlighten: Theses (The University of Glasgow) · Cited by 2
Why BCIs work poorly with the patients who need them the most?Preprint
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Brain–Computer Interfaces and Their Place in the Management of Disorders of Consciousness
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How to build a brain–computer interface from beginning to end
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Two Seconds to Speak: Increasing Communication Speed for fMRI-Based Brain–Computer Interfaces
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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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