Co-adaptive calibration to improve BCI efficiency
Open accessAbstract
All brain-computer interface (BCI) groups that have published results of studies involving a large number of users performing BCI control based on the voluntary modulation of sensorimotor rhythms (SMR) report that BCI control could not be achieved by a non-negligible number of subjects (estimated 20% to 25%). This failure of the BCI system to read the intention of the user is one of the greatest problems and challenges in BCI research. There are two main causes for this problem in SMR-based BCI systems: either no idle SMR is observed over motor areas of the user, or this idle rhythm is not modulated during motor imagery, resulting in a classification performance lower than 70% (criterion level) that renders the control of a BCI application (like a speller) difficult or impossible. Previously, we introduced the concept of machine learning based co-adaptive calibration, which provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adaptive learning enables significant BCI control for completely novice users, as well as for those who could not achieve control with a conventional SMR-based BCI.
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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 [28]
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 [28]
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- Authors
- Vidaurre, C., Sannelli, C., Müller, K.-R., & Blankertz, B.
- Affiliations
- Technische Universität Berlin (DE)
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2011-03-24
- Volume
- 8
- Issue
- 2
- Pages
- 025009
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W1969223073
- Cited by
- 169
- References
- 32
- Citations by year
- 2026: 9 · 2025: 4 · 2024: 7 · 2023: 9 · 2022: 6 · 2021: 9 · 2020: 14 · 2019: 11 · 2018: 18 · 2017: 13 · 2016: 15 · 2015: 15 · 2014: 14 · 2013: 11 · 2012: 12
- FWCI
- 6.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neural Engineering (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)
- Funders
- 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 › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- brain-computer interface, sensorimotor rhythm, motor imagery, novice users, speller
- MeSH terms
- Algorithms, Brain Mapping, Calibration, Electroencephalography, Evoked Potentials, Somatosensory, Humans, Motor Cortex, Somatosensory Cortex, User-Computer Interface
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