The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces
Open accessAbstract
Brain-computer interfaces (BCIs) allow users to control a computer application by brain activity as acquired (e.g., by EEG). In our classic machine learning approach to BCIs, the participants undertake a calibration measurement without feedback to acquire data to train the BCI system. After the training, the user can control a BCI and improve the operation through some type of feedback. However, not all BCI users are able to perform sufficiently well during feedback operation. In fact, a nonnegligible portion of participants (estimated 15%–30%) cannot control the system (a BCI illiteracy problem, generic to all motor-imagery-based BCIs). We hypothesize that one main difficulty for a BCI user is the transition from offline calibration to online feedback. In this work, we investigate adaptive machine learning methods to eliminate offline calibration and analyze the performance of 11 volunteers in a BCI based on the modulation of sensorimotor rhythms. We present an adaptation scheme that individually guides the user. It starts with a subject-independent classifier that evolves to a subject-optimized state-of-the-art classifier within one session while the user interacts continuously. These initial runs use supervised techniques for robust coadaptive learning of user and machine. Subsequent runs use unsupervised adaptation to track the features’ drift during the session and provide an unbiased measure of BCI performance. Using this approach, without any offline calibration, six users, including one novice, obtained good performance after 3 to 6 minutes of adaptation. More important, this novel guided learning also allows participants with BCI illiteracy to gain significant control with the BCI in less than 60 minutes. In addition, one volunteer without sensorimotor idle rhythm peak at the beginning of the BCI experiment developed it during the course of the session and used voluntary modulation of its amplitude to control the feedback application.
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“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 [26]
In the citing paper
“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 [26]
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Cites
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“…EEG features of large complexity that are fitted better to the individual characteristics of brain patterns of each user (see Blankertz et al., 2007; Blankertz, Tomioka, et al., 2008; Dornhege, Blankertz, Curio, & M¨uller, 2004; Dornhege et al., 2007; M¨uller et al., 2008; M¨uller, Anderson, &…”
BackgroundMethodInfluentialSelf-citationLearning to control a brain–machine interface for reaching and grasping by primates
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Research works printed but not linked: 2
- Blankertz B., 2010, Detecting mental states by machine learning techniques, 113
- Sugiyama M., 2007, Journal of Machine Learning Research, 8, 1027
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NeuroCommTrainer: Toward an Adaptive and Wearable Multimodal Brain–Computer Interface
Scherer, J., Finke, A., Everding, V., Lindenbaum, L., Kayser, C., & Kissler, J. M. (2025) · Brain Connectivity · doi:10.1177/21580014251393151
Development and study of coadaptive interactive systems
Dechaux, A. (2024) · theses.fr (ABES) · doi:10.70675/24336488z62bdz4b32zbc69z1620e376bc34
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Human Factors in Brain–Computer Interfaces: Boundaries, Key Issues, and Methodological Approaches
Yan, L., Li, T., Zhao, L., Gong, A., Hao, F., Zuo, W., & Fu, Y. (2026) · Brain Sciences · doi:10.3390/brainsci16101066
Metadata
- Authors
- Vidaurre, C., Sannelli, C., Müller, K.-R., & Blankertz, B.
- Affiliations
- Technische Universität Berlin (DE)
- Corresponding author
- Vidaurre, Carmen
- Sannelli, Claudia
- Müller, Klaus-Robert
- Blankertz, Benjamin
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 2011-03
- Volume
- 23
- Issue
- 3
- Pages
- 791-816
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2068654413
- Cited by
- 207
- References
- 50
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 6 · 2023: 9 · 2022: 5 · 2021: 10 · 2020: 13 · 2019: 14 · 2018: 19 · 2017: 13 · 2016: 18 · 2015: 13 · 2014: 13 · 2013: 25 · 2012: 22
- FWCI
- 11.80
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- other-oa
- Locations
- Neural Computation (publishedVersion)
- PubMed (publishedVersion)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, public-domain)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- 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
- brain-computer interface, BCI illiteracy, motor imagery, sensorimotor rhythm, online adaptation, unsupervised adaptation, feature drift, EEG, novice users
- MeSH terms
- Adaptation, Physiological, Adaptation, Psychological, Algorithms, Artificial Intelligence, Brain, Calibration, Electroencephalography, Humans, Neuronal Plasticity, Feedback, Psychological, Brain-Computer Interfaces
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