Context-aware adaptive spelling in motor imagery BCI
Open accessAbstract
Objective. This work presents a first motor imagery-based, adaptive brain–computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ten able-bodied users evaluate the ability of our scheme, first, to alleviate non-stationarity of brain signals for restoring the subject’s performances, second, to guide naive users into BCI control avoiding initial offline BCI calibration and, third, to outperform regular unsupervised adaptation. Approach. Our co-adaptive framework combines the BrainTree speller with smooth-batch linear discriminant analysis adaptation. The latter enjoys contextual assistance through BrainTree’s language model to improve online expectation-maximization maximum-likelihood estimation. Main results. Our results verify the possibility to restore single-sample classification and BCI command accuracy, as well as spelling speed for expert users. Most importantly, context-aware adaptation performs significantly better than its unsupervised equivalent and similar to the supervised one. Although no significant differences are found with respect to the state-of-the-art PMean approach, the proposed algorithm is shown to be advantageous for 30% of the users. Significance. We demonstrate the possibility to circumvent supervised BCI recalibration, saving time without compromising the adaptation quality. On the other hand, we show that this type of classifier adaptation is not as efficient for BCI training purposes.
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The authors' previous work reported a short-term decrease in feature discriminancy during adaptive spelling.
“Our previous work has even reported a short-term decrease in feature discriminancy during adaptive spelling [29].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [29]
Better BCI performance could be due to decoder recalibration, re-parameterizations of the BCI, and the application and adoption of better mental strategies, among other factors.
“Better performance could be due to decoder recalibration [29], re-parameterizations of the BCI, and the application and adoption of better mental strategies [9,64,65], among other factors.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [29]
Frequent or continuous recalibration, especially when accompanied by re-selecting the classifier's features, creates a situation in which the subject's learning could be hindered by the demand to adapt to a continuously changing decoder.
“Frequent or continuous recalibration, especially in case it is accompanied by re-selecting the classifier’s features, creates a situation in which the subject’s learning could be hindered by the demand to adapt to a continuously changing decoder [29,70].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [29]
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The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users
Perdikis, S., Tonin, L., Saeedi, S., Schneider, C., & Millán, J. D. R. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2003787 · Cited by 166 · see citing works
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- Authors
- Perdikis, S., Leeb, R., & Millán, J. D. R.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Perdikis, S
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2016-05-06
- Volume
- 13
- Issue
- 3
- Pages
- 036018
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2346898728
- Cited by
- 49
- References
- 54
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 9 · 2023: 3 · 2022: 4 · 2021: 5 · 2020: 7 · 2019: 3 · 2018: 5 · 2017: 7
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Open Access at Essex (University of Essex) (submittedVersion)
- Funders
- Hasler Stiftung
- Seventh Framework Programme (FP7-224631 TOBI)
- 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
- motor imagery, brain-computer interface, contextual adaptation, classifier adaptation, linear discriminant analysis, language models, expectation-maximization algorithm
- MeSH terms
- Adaptation, Physiological, Adult, Algorithms, Awareness, Communication Devices for People with Disabilities, Electroencephalography, Female, Humans, Language, Male, Psychomotor Performance, Reaction Time, Reproducibility of Results, Discriminant Analysis, Brain-Computer Interfaces
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