Principal component based covariate shift adaption to reduce non-stationarity in a MEG-based brain-computer interface
Principal component based covariate shift adaption to reduce non-stationarity in a MEG-based brain-computer interface
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DOI：
10.1186/1687-6180-2012-129
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发表时间：
2012-01-01
影响因子：
1.9
通讯作者：
Bogdan, Martin
中科院分区：
文献类型：
--
作者：
Spueler, Martin;Rosenstiel, Wolfgang;Bogdan, Martin
当今脑-机接口研究中最大的问题之一是记录信号的非平稳性。这种非平稳性会导致BCI性能随着时间的推移而恶化，或者在将数据从一个会话传输到另一个会话时显著下降。为了减少非平稳性的影响，我们提出了一种基于主成分分析的协变量平移自适应方法来提取和消除非平稳性。在离线分析和10个受试者的在线实验中，我们展示了所提出的方法显著提高了基于脑磁图的脑机接口的性能。我们还证明了该方法优于其他协变量平移自适应方法，并给出了识别非平稳序列的例子来说明该方法的效果。
One of the biggest problems in today's BCI research is the non-stationarity of the recorded signals. This non-stationarity can cause the BCI performance to deteriorate over time or drop significantly when transferring data from one session to another. To reduce the effect of non-stationaries, we propose a new method for covariate shift adaption that is based on Principal Component Analysis to extract non-stationaries and alleviate them. We show the proposed method to significantly increase BCI performance for an MEG-based BCI in an offline analysis as well as an online experiment with 10 subjects. We also show the method to be superior to other covariate shift adaption methods and present examples of identified non-stationaries to show the effect of the proposed method.