Classifying four-category visual objects using multiple ERP components in single-trial ERP
Classifying four-category visual objects using multiple ERP components in single-trial ERP
复制标题
在单次试验 ERP 中使用多个 ERP 组件对四类视觉对象进行分类
DOI：
10.1007/s11571-016-9378-0
复制
发表时间：
2016
影响因子：
3.7
通讯作者：
Hu Bin
中科院分区：
文献类型：
--
作者：
Qin Yu;Zhan Yu;Wang Changming;Zhang Jiacai;Yao Li;Guo Xiaojuan;Wu Xia;Hu Bin
使用参与者观看图像时测量的单次试验脑电图 (EEG) 数据进行对象分类已得到深入研究。在之前的研究中，多个事件相关电位（ERP）组件（例如P1、N1、P2和P3）被用来提高视觉刺激的对象分类的性能。在本研究中，我们介绍了一种使用多内核支持向量机融合多个 ERP 组件特征的新颖方法。我们研究融合不同 ERP 组件（例如 P1、N1、P2a 和 P2b）的潜在互补信息是否可以提高单次试验 EEG 中四类视觉对象分类的性能。我们还比较了不同 ERP 组件融合方法的分类准确性。我们的实验结果表明，通过多个 ERP 融合可以提高分类精度。其他比较分析表明，多核融合方法可以实现高于 72% 的平均分类精度，这明显优于任何单个 ERP 组件功能所实现的分类精度（最佳单个 ERP 组件 N1 为 55.07%）。我们将分类结果与其他融合方法的分类结果进行比较，确定多核融合方法的准确率分别比特征连接、特征提取和决策融合的准确率高 5.47%、4.06% 和 16.90%。我们的研究表明，我们的多核融合方法优于其他融合方法，从而为提高脑机接口研究中单试验 ERP 的分类性能提供了一种方法。
Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiple-kernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiple-kernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain–computer interface research.