CAJ | 학술논문

针对基于运动想象的脑机接口问题，提出了一种新的特征提取方法，即加强的滤波带宽共同空间模式方法。与传统的滤波带宽共同空间模式方法相比，文中提出的方法充分考虑了被试在进行运动想象时发生事件相关去同步时段的特异性，从而得到更多的特征，并利用这些特征分类，取得更高的准确率和kappa值。在特征的选择上，利用BCI的竞赛数据，将基于互信息的方法和基于准确率的方法进行对比，发现基于准确率的特征选择方法优于基于互信息的特征选择方法。
침대기우운동상상적뇌궤접구문제，제출료일충신적특정제취방법，즉가강적려파대관공동공간모식방법。여전통적려파대관공동공간모식방법상비，문중제출적방법충분고필료피시재진행운동상상시발생사건상관거동보시단적특이성，종이득도경다적특정，병이용저사특정분류，취득경고적준학솔화kappa치。재특정적선택상，이용BCI적경새수거，장기우호신식적방법화기우준학솔적방법진행대비，발현기우준학솔적특정선택방법우우기우호신식적특정선택방법。
This paper proposes the augmented Filter Bank Common Spatial Pattern, a novel feature extraction al- gorithm for the motor imagery-based brain-computer interface. Compared with the Filter Bank Common Spatial Pattern, our method takes into full consideration the specificity temporal information, in which ERD happens in motor imagery. In this way, more features are acquired, and the higher accuracy and kappa value are obtained by these features. Two kinds of feature selection algorithms are employed in the BCI competition dataset. The results show that the accuracy based method yields superior classification accuracy compared with that based on mutual information.