Research Repository

Context-aware adaptive spelling in motor imagery BCI

Perdikis, S and Leeb, R and Millán, JDR (2016) 'Context-aware adaptive spelling in motor imagery BCI.' Journal of Neural Engineering, 13 (3). 036018-036018. ISSN 1741-2552

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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.

Item Type: Article
Uncontrolled Keywords: brain-computer interface; unsupervised adaptation; text-speller; motor imagery; context-awareness; online learning
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 13 Aug 2019 12:21
Last Modified: 23 Sep 2022 19:30

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