Abstract: Transfer learning, as a new machine learning methodology, may solve problems in related but different domains by using existing knowledge, and it is often applied to transfer training data from another domain for model training in the case of insufficient training data. In recent years, an increasing number of researchers who engage in brain-computer interface (BCI), have focused on using transfer learning to make most of the available electroencephalogram data from different subjects, effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the model. This paper surveys the development of transfer learning and reviews the transfer learning approaches in BCI. In addition, according to the “what to transfer” question in transfer learning, this review is organized into three contexts: instance-based transfer learning, parameter-based transfer learning, and feature-based transfer learning. Furthermore, the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods, datasets, evaluation performance, etc. At the end of the paper, the questions to be solved in future research are put forward, laying the foundation for the popularization and in-depth research of transfer learning in BCI.

Key words: transfer learning, brain-computer interface (BCI), electroencephalogram, machine learning

摘要： 迁移学习是一种新的机器学习方法，能够利用现有知识解决相关但不相同域的问题。在训练数据不足的情况下，它常将另一个域的训练数据进行迁移用于模型训练。近年来，越来越多的脑机接口研究者关注迁移学习，充分利用从不同受试者获取的脑电数据，有效降低数据采集和标签的成本，同时极大地改善模型学习性能。本文对迁移学习的发展进行调查，并综述了脑机接口中的迁移学习方法。依据迁移学习中“迁移什么”问题，本文分为三部分：基于实例的迁移学习、基于参数的迁移学习和基于特征的迁移学习。另外，将目前脑机接口研究中的迁移学习应用情况从迁移学习方法、数据库和性能评价等方面进行概述。最后，提出未来研究要解决的问题，对脑机接口中迁移学习的普及与深入研究奠定了基础。

关键词: 迁移学习，脑机接口，脑电图，机器学习