Spüler Martin, Walter Armin, Rosenstiel Wolfgang, Bogdan Martin
IEEE Trans Neural Syst Rehabil Eng. 2014 Nov;22(6):1097-103. doi: 10.1109/TNSRE.2013.2290870. Epub 2013 Nov 20.
Classification of evoked or event-related potentials is an important prerequisite for many types of brain-computer interfaces (BCIs). To increase classification accuracy, spatial filters are used to improve the signal-to-noise ratio of the brain signals and thereby facilitate the detection and classification of evoked or event-related potentials. While canonical correlation analysis (CCA) has previously been used to construct spatial filters that increase classification accuracy for BCIs based on visual evoked potentials, we show in this paper, how CCA can also be used for spatial filtering of event-related potentials like P300. We also evaluate the use of CCA for spatial filtering on other data with evoked and event-related potentials and show that CCA performs consistently better than other standard spatial filtering methods.
诱发电位或事件相关电位的分类是许多类型脑机接口（BCI）的重要前提。为了提高分类准确率，空间滤波器被用于改善脑信号的信噪比，从而便于诱发电位或事件相关电位的检测与分类。虽然典型相关分析（CCA）此前已被用于构建空间滤波器，以提高基于视觉诱发电位的脑机接口的分类准确率，但我们在本文中展示了，CCA 如何也可用于对 P300 等事件相关电位进行空间滤波。我们还评估了 CCA 在其他具有诱发电位和事件相关电位的数据上进行空间滤波的应用，并表明 CCA 的性能始终优于其他标准空间滤波方法。
