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.

翻译：脑计算机界面( BCI) 提供了人类大脑和外部设备之间的直接沟通路径。 在新对象使用 BCI 之前, 通常需要一个校准程序。 因为受试对象之间和内部的差异很大, 现有对象所培训的模型在新对象上表现不佳。 因此, 有效的主题转移和校准方法至关重要 。 在本文件中, 我们提议了一种半监督的元学习方法, 用于在BCI 中进行主题转移学习。 拟议的SSML首先学习与现有对象的元模型, 然后以半监督的学习方式微调模型, 即使用很少的标签和许多没有标签的标本样本进行校准。 对于BCI 应用程序来说意义重大, 标签数据稀缺或昂贵, 而没有标签的数据很容易获得。 为了验证 校准 SSML 方法, 测试了三种不同的 BCI 模式:(1) 与事件有关的潜在检测;(2) 情绪识别;和(3) 睡眠状态。 SSML 在前两个范例上取得了超过15%的显著改进, 在第三个模型上改进了4.9%。 实验结果展示了 SSML 方法在 B 应用的有效性和潜力。