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

A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface