期刊
IEEE ACCESS
卷 7, 期 -, 页码 49951-49960出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2908851
关键词
Brain-computer interface EEG; transfer learning; boosting; domain transfer multiple kernel boosting
资金
- National Natural Science Foundation of China [61873021]
- Youth Talent Support Program of Beihang University
- National Key Scientific Instrument and Equipment Development Project [2014YQ350461]
The application of wireless sensors in the brain-computer interface (BCI) system provides great convenience for the acquisition of electroencephalography (EEG) signals. However, a large amount of training data is needed to build the classification architectures used in motor imagery (MI) brain-computer interface (BCI), which is time-consuming to generate. To address this issue, transfer learning has gained significant attention in a small sample setting BCI system. The transfer learning methods have shown promising results by leveraging labeled patterns from the source domain to learn robust classifiers for the target domain, which has only a limited number of labeled samples. However, the successful application of such approaches in a motor imagery BCI remains limited. In this paper, we present a novel framework called domain transfer multiple kernel boosting (DTMKB), which extends the DTMKL algorithms by applying boosting techniques for learning kernel-based classifiers with the transfer of multiple kernels. Based on the proposed framework, we examined their empirical performance in comparison to several state-of-theart algorithms on two MI task datasets. DTMKB yields the best performance for all datasets and achieves the best average classification accuracy 87.60%, 76.00%, 74.66%, and 74.13%, respectively. In particular, the proposed framework can be applied successfully in a small sample of EEG motor imagery signals.
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