Yuksel Ayhan, Olmez Tamer
Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey.
PLoS One. 2015 May 1;10(5):e0125039. doi: 10.1371/journal.pone.0125039. eCollection 2015.
In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN) is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP) algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.
在本研究中，介绍了一种新颖的空间滤波器设计方法。空间滤波是基于运动想象的脑机接口中特征提取的重要处理步骤。本文介绍了一种结合空间滤波器优化的新型运动想象信号分类方法。我们使用神经网络方法同时训练空间滤波器和分类器。所提出的空间滤波器网络（SFN）由两层组成：空间滤波层和分类器层。这两层通过非线性映射函数相互连接。所提出的方法解决了共同空间模式（CSP）算法的两个缺点。首先，CSP旨在最大化类间方差，而忽略类内方差的最小化。因此，使用CSP方法获得的特征可能具有较大的类内方差。其次，CSP的最大化优化函数间接提高了分类准确率，因为在CSP方法之后使用了独立的分类器。使用SFN，我们旨在最大化类间方差，同时最小化类内方差，并同时优化空间滤波器和分类器。为了对运动想象脑电信号进行分类，我们修改了著名的前馈结构，并推导了与所提出结构相对应的正向和反向方程。我们在简单的玩具数据上测试了我们的算法。然后，我们在BCI竞赛III的两个数据集上，将SFN与传统的CSP及其多类版本（称为一对其余CSP）进行了比较。评估结果表明，SFN是一种用于分类运动想象脑电信号的良好替代方法，具有更高的分类准确率。
