Background

To reduce calibration time of brain-computer interface (BCI) or even implement zero-training BCI, researchers have been studying how to effectively apply transfer learning in the field. In order to thoroughly investigate the performance of transfer learning in BCI and the key factors affecting transfer performance in the field, we carried out a comprehensive study.

New Method

In general, transferring knowledge in BCI is implemented in two ways: ensemble or pool. In this work, we propose two different transfer approaches. One is to transfer the information of all channels as a whole from the source subjects to a target subject. The second approach is to transfer the information of corresponding channels between the subjects. A subject transfer framework is built by combining the two approaches with ensemble or pool.

Results

We investigated the performances of eight implementations of this framework on a data set acquired by an interpersonal interaction (Chicken Game) experiment based on code-modulated visual evoked potential (c-VEP) BCI. The results show that transfer learning generally provides acceptable classification performance. Additionally, an in-depth analysis reveals that a target subject usually shares different brain signal distribution with different source subjects. In fact, this is a hypothesis usually implied by this kind of research.

Conclusions

Transfer learning for c-VEP BCI can be qualified for reducing calibration time or starting the recognition of BCI without sufficient subjects’ own data. In addition, our finding suggests a solid validity of the hypothesis underlying transferring knowledge in BCI.

中文翻译：

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合奏或集合：人际互动过程中c-VEP BCI迁移学习的综合研究。

背景

为了减少脑机接口（BCI）的校准时间甚至实施零培训BCI，研究人员一直在研究如何在现场有效地应用转移学习。为了深入研究BCI中的转移学习成绩以及影响该领域转移成绩的关键因素，我们进行了全面的研究。

新方法

通常，以两种方式实施BCI中的知识转移：集成或合并。在这项工作中，我们提出了两种不同的转移方法。一种是将所有通道的信息整体从源主题传输到目标主题。第二种方法是在受试者之间传递相应频道的信息。主题转移框架是通过将两种方法与合奏或集合相结合而构建的。

结果

我们在基于代码调制的视觉诱发电位（c-VEP）BCI的人际互动（鸡游戏）实验获得的数据集上调查了该框架的八个实现的性能。结果表明，转移学习通常提供可接受的分类性能。此外，深入分析显示目标对象通常与不同的源对象共享不同的脑信号分布。实际上，这是这种研究通常所暗含的假设。

结论

c-VEP BCI的传递学习可以减少校准时间或在没有足够受试者自身数据的情况下开始识别BCI。另外，我们的发现表明，在BCI中转移知识的假设具有坚实的依据。