标题： 在脑机接口中跨受试者的子空间迁移 显示英文标题

标题： Transferring Subspaces Between Subjects in Brain-Computer Interfacing

摘要： 在脑机接口(BCI)中，受试者训练和测试会话之间的补偿变化具有挑战性，但对于稳健的BCI操作非常重要。我们表明，这些变化在受试者之间非常相似，因此可以使用其他用户的数据可靠地估计，并用于构建不变特征空间。这种从其他受试者学习的新方法旨在减少常见非平稳性的不利影响，但不传递判别信息。这与标准多受试者方法有重要的概念差异，例如通过将协方差矩阵收缩到其他用户的平均值来提高协方差矩阵估计，或构建一个全局特征空间。这些方法不会减少训练数据和测试数据之间的偏移，并且当受试者的信号特征差异很大时可能会产生较差的结果。在本文中，我们在玩具数据和两个受试者执行运动想象的EEG记录数据集上将我们的方法与两种最先进的多受试者方法进行比较。我们证明，它不仅可以显著提高性能，而且提取的变化模式还允许神经生理学上有意义的解释。 显示英文摘要

摘要： Compensating changes between a subjects' training and testing session in Brain Computer Interfacing (BCI) is challenging but of great importance for a robust BCI operation. We show that such changes are very similar between subjects, thus can be reliably estimated using data from other users and utilized to construct an invariant feature space. This novel approach to learning from other subjects aims to reduce the adverse effects of common non-stationarities, but does not transfer discriminative information. This is an important conceptual difference to standard multi-subject methods that e.g. improve the covariance matrix estimation by shrinking it towards the average of other users or construct a global feature space. These methods do not reduces the shift between training and test data and may produce poor results when subjects have very different signal characteristics. In this paper we compare our approach to two state-of-the-art multi-subject methods on toy data and two data sets of EEG recordings from subjects performing motor imagery. We show that it can not only achieve a significant increase in performance, but also that the extracted change patterns allow for a neurophysiologically meaningful interpretation.