School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.
Agency for Science, Technology and Research, Institute for Infocomm Research, Singapore.
IEEE Trans Neural Netw Learn Syst. 2017 Nov;28(11):2727-2737. doi: 10.1109/TNNLS.2016.2601084.
To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.
为了检测感兴趣的心理任务，空间滤波已被广泛用于提高脑电图（EEG）的空间分辨率。然而，由于 EEG 的显著非平稳性，空间滤波的有效性受到了影响。基于正则化，大多数传统的静态空间滤波器设计方法都以牺牲类间可分性为代价来解决非平稳性问题。此外，由于正则化，当 EEG 协方差矩阵不能联合对角化时，空间滤波器的优化与特征提取不一致。在本文中，我们提出了一种新的空间滤波器设计框架。我们直接将特征空间中的 Fisher 比用作目标函数，将空间滤波器的优化与特征提取统一起来。由于其比值形式，避免了正则化参数的选择。我们在一个由 16 名受试者组成的二进制运动想象数据集上评估了所提出的方法，这些受试者在不同的日子进行了校准和测试。实验结果表明，与传统的非统一方法相比，所提出的方法在单宽带和滤波器组设置下都提高了分类性能。我们还通过模拟研究系统地尝试了比较不同的目标函数在数据非平稳性建模中的作用。
