脑-机接口中主体间的子空间转换
Transferring Subspaces Between Subjects in Brain-Computer Interfacing
摘要：
在脑机接口（BCI）中，补偿受试者的训练和测试会话之间的变化是具有挑战性的，但对于鲁棒的BCI操作非常重要。我们表明，这种变化是非常相似的主题之间，因此可以可靠地估计使用其他用户的数据，并利用构建一个不变的特征空间。这种从其他学科学习的新方法旨在减少常见的非平稳性的不利影响，但不会传递歧视性信息。这是与标准多对象方法的重要概念差异，标准多对象方法例如通过将协方差阵估计向其他用户的平均值收缩来改进协方差矩阵估计或构造全局特征空间。这些方法不能减少训练数据和测试数据之间的偏移，并且当受试者具有非常不同的信号特征时，可能会产生较差的结果。在本文中，我们比较了我们的方法，以两个国家的最先进的多学科方法的玩具数据和两个数据集的EEG记录从主题进行运动想象。我们表明，它不仅可以实现显着提高性能，而且提取的变化模式允许神经生理学意义的解释。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
