The performance of a brain–computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier’s generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibration time in BCI systems by proposing a transfer learning model which can be used for evaluating unseen single trials for a subject without the need for training session data. A method is proposed which combines a generalization of the previously proposed subject-specific “multivariate empirical-mode decomposition” preprocessing technique by taking a fixed band of 8–30[Formula: see text]Hz for all four motor imagery tasks and a novel classification model which exploits the structure of tangent space features drawn from the Riemannian geometry framework, that is shared among the training data of multiple sessions and subjects. Results demonstrate comparable performance improvement across multiple subjects without subject-specific calibration, when compared with other state-of-the-art techniques.

中文翻译：

---

基于切线空间特征的二类运动图像脑机接口迁移学习分类模型

脑机接口 (BCI) 的性能通常会通过增加训练数据量来提高。然而，当跨会话和主题收集高度非平稳的数据时，分类器的泛化能力通常会受到负面影响。这项工作的目的是通过提出一种迁移学习模型来减少 BCI 系统中较长的校准时间，该模型可用于评估受试者的未见单次试验，而无需训练会话数据。提出了一种方法，该方法通过采用 8-30 [公式：请参阅文本]Hz 用于所有四个运动图像任务和一个新颖的分类模型，该模型利用从黎曼几何框架中提取的切线空间特征的结构，该模型在多个会话和主题的训练数据之间共享。结果表明，与其他最先进的技术相比，在没有特定主题校准的情况下，多个主题的性能改进相当。