Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8503, Japan.
IEEE Trans Biomed Eng. 2010 Jun;57(6):1318-24. doi: 10.1109/TBME.2009.2039997. Epub 2010 Feb 17.
A phenomenon often found in session-to-session transfers of brain-computer interfaces (BCIs) is nonstationarity. It can be caused by fatigue and changing attention level of the user, differing electrode placements, varying impedances, among other reasons. Covariate shift adaptation is an effective method that can adapt to the testing sessions without the need for labeling the testing session data. The method was applied on a BCI Competition III dataset. Results showed that covariate shift adaptation compares favorably with methods used in the BCI competition in coping with nonstationarities. Specifically, bagging combined with covariate shift helped to increase stability, when applied to the competition dataset. An online experiment also proved the effectiveness of bagged-covariate shift method. Thus, it can be summarized that covariate shift adaptation is helpful to realize adaptive BCI systems.
在脑机接口（BCI）的会话间转移中，经常会出现非平稳现象。它可能是由用户的疲劳和注意力水平变化、不同的电极放置位置、不同的阻抗等原因引起的。协变量偏移自适应是一种有效的方法，可以在不需要对测试会话数据进行标记的情况下适应测试会话。该方法应用于 BCI 竞赛 III 数据集。结果表明，协变量偏移自适应方法在应对非平稳性方面优于 BCI 竞赛中使用的方法。具体来说，当应用于竞赛数据集时，袋装结合协变量偏移有助于提高稳定性。在线实验也证明了袋装协变量偏移方法的有效性。因此，可以总结出协变量偏移自适应有助于实现自适应 BCI 系统。
