Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
IEEE Trans Biomed Eng. 2013 Apr;60(4):1100-10. doi: 10.1109/TBME.2012.2215960. Epub 2012 Aug 29.
The spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery-based brain-computer interface (MI-BCI). To achieve accurate classification in CSP, it is necessary to find frequency bands that relate to brain activities associated with BCI tasks. Several methods that determine such a set of frequency bands have been proposed. However, the existing methods cannot find the multiple frequency bands by using only learning data. To address this problem, we propose discriminative filter bank CSP (DFBCSP) that designs finite impulse response filters and the associated spatial weights by optimizing an objective function which is a natural extension of that of CSP. The optimization is conducted by sequentially and alternatively solving subproblems into which the original problem is divided. By experiments, it is shown that DFBCSP can effectively extract discriminative features for MI-BCI. Moreover, experimental results exhibit that DFBCSP can detect and extract the bands related to brain activities of motor imagery.
电极的空间权重（称为共空间模式（CSP））被认为在基于运动想象的脑机接口（MI-BCI）的 EEG 信号分类中是有效的。为了在 CSP 中实现准确的分类，有必要找到与与 BCI 任务相关的大脑活动相关的频带。已经提出了几种确定这样的频带集的方法。然而，现有的方法不能仅使用学习数据找到多个频带。为了解决这个问题，我们提出了判别滤波器组 CSP（DFBCSP），它通过优化目标函数来设计有限脉冲响应滤波器和相关的空间权重，该目标函数是 CSP 的自然扩展。优化通过顺序和交替地求解原始问题分为子问题来进行。通过实验表明，DFBCSP 可以有效地提取 MI-BCI 的判别特征。此外，实验结果表明，DFBCSP 可以检测和提取与运动想象的大脑活动相关的频带。
