Journal
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume 29, Issue 10, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065719500254
Keywords
Motor imagery; brain-computer interface (BCI); tangent space; covariance matrix; multivariate empirical-mode decomposition (MEMD); subject-specific multivariate empirical-mode decomposition-based filtering (SS-MEMDBF)
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Funding
- Ulster University Vice Chancellors Research Scholarship (VCRS)
- Northern Ireland Functional Brain Mapping Facility [303/101154803, 1303/101154803]
- UKIERI DST Thematic Partnership [DST-UKIERI-2016-17-0128]
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The performance of a brain-computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier's generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibration time in Bel systems by proposing a transfer learning model which can be used for evaluating unseen single trials for a subject without the need for training session data. A method is proposed which combines a generalization of the previously proposed subject-specific multivariate empirical-mode decomposition preprocessing technique by taking a fixed band of 8-30 Hz for all four motor imagery tasks and a novel classification model which exploits the structure of tangent space features drawn from the Riemannian geometry framework, that is shared among the training data of multiple sessions and subjects. Results demonstrate comparable performance improvement across multiple subjects without subject-specific calibration, when compared with other state-of-the-art techniques.
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