  Brain-computer interface (BCI) provides a direct communication pathway
between human brain and external devices. Before a new subject could use BCI, a
calibration procedure is usually required. Because the inter- and intra-subject
variances are so large that the models trained by the existing subjects perform
poorly on new subjects. Therefore, effective subject-transfer and calibration
method is essential. In this paper, we propose a semi-supervised meta learning
(SSML) method for subject-transfer learning in BCIs. The proposed SSML learns a
meta model with the existing subjects first, then fine-tunes the model in a
semi-supervised learning manner, i.e. using few labeled and many unlabeled
samples of target subject for calibration. It is significant for BCI
applications where the labeled data are scarce or expensive while unlabeled
data are readily available. To verify the SSML method, three different BCI
paradigms are tested: 1) event-related potential detection; 2) emotion
recognition; and 3) sleep staging. The SSML achieved significant improvements
of over 15% on the first two paradigms and 4.9% on the third. The experimental
results demonstrated the effectiveness and potential of the SSML method in BCI
applications.
