Qian Ming, Aguilar Mario, Zachery Karen N, Privitera Claudio, Klein Stanley, Carney Thom, Nolte Loren W
Teledyne Scientific and Imaging LLC, Research Triangle Laboratory, Durham, NC 27713, USA.
IEEE Trans Biomed Eng. 2009 Jul;56(7):1929-37. doi: 10.1109/TBME.2009.2016670. Epub 2009 Mar 27.
Several recent studies have reported success in applying EEG-based signal analysis to achieve accurate single-trial classification of responses to visual target detection. Pupil responses are proposed as a complementary modality that can support improved accuracy of single-trial signal analysis. We develop a pupillary response feature-extraction and -selection procedure that helps to improve the classification performance of a system based only on EEG signal analysis. We apply a two-level linear classifier to obtain cognitive-task-related analysis of EEG and pupil responses. The classification results based on the two modalities are then fused at the decision level. Here, the goal is to support increased classification confidence through the inherent modality complementarities. The fusion results show significant improvement over classification performance based on a single modality.
最近的几项研究报告称，在应用基于脑电图（EEG）的信号分析以实现对视觉目标检测反应的准确单次试验分类方面取得了成功。瞳孔反应被提议作为一种补充方式，可支持提高单次试验信号分析的准确性。我们开发了一种瞳孔反应特征提取和选择程序，有助于提高仅基于EEG信号分析的系统的分类性能。我们应用两级线性分类器来获得与认知任务相关的EEG和瞳孔反应分析。然后在决策层面融合基于这两种方式的分类结果。在此，目标是通过固有的方式互补性来提高分类置信度。融合结果显示，与基于单一方式的分类性能相比有显著提高。
