【摘要】 在BCI (Brain computer interface)的研究中,导联选择能够用于确定与目标任务关联较大的脑功能区域。以往的导联选择方法都是基于一个数据集进行统一的导联选择,不同任务下的对应脑区是不一样的,无法抑制同一数据集中不同任务特征的干扰。基于运动想象脑电数据展开相关研究,利用SVM_RFE (Support vector machine recursive feature elimination)导联选择方法以召回率作为依据为两类运动任务分别选择最适合的导联。研究结果表明,基于SVM_RFE多任务导联选择方法在平均分类准确率上优于传统的SVM_RFE导联选择方法。更多还原

【Abstract】 Channel selection is used to locate the mental task related brain area in the brain computer interface systems. In the previous studies, the channels were selected based on the data recoded from the multi-mental tasks. However, the different mental tasks are corresponding to the different brain area. If one same brain area is selected for different mental tasks, the features of one mental task would be noises of another mental task. In this paper, a new method was presented to solve this problem based on the data of motor imagery tasks. The SVM-RFE method was used to select the channels for each motor imagery task based on recall rate. The result showed that the proposed channel selection method was superior to the traditional method in classification accuracy.更多还原