摘要
针对异步脑机接口(BCI)中空闲状态难以检测的问题,提出将近似熵与公共空间模式(CSP)综合的方法来处理.在采用二级分类策略的前提下,通过近似熵与CSP方法分别从时间复杂度和空间模式上提取不同类型的脑电特征,利用这些特征训练出不同的分类器,然后使用多分类器投票的方法将它们综合以提高判断空闲状态的正确率.将本文的方法运用到BCI竞赛数据中,得到最终具体想象任务的命中率(TPR)普遍比通过阈值法得到的结果要高.数据处理的结果说明了本文方法对空闲状态检测的有效性.
To cope with the issue of the idle state detection which is difficult in motor imagery based brain-computer interface, the paper proposes a method that approximate entropy and common spatial pattern（CSP） are combined. On the condition of two-class classification, different kings of features are extracted through approximate entropy in time complexity and CSP in spatial pattern. Then these features are used to make different classifiers which are combined by vote-based classification method to improve the accuracy of judging idle state. By way of this method, the final experimental results of BCI competition shows the true positive rate（TPR） of intentional motor imagery is higher than the threshold method. The result of data processing indicates the effectiveness of the proposed method.
出处
《计算机系统应用》
2014年第6期153-157,共5页
Computer Systems & Applications
关键词
异步脑机接口
空闲状态
近似熵
共同空间模式
多分类器投票法
asynchronous brain-computer interface
idea state
approximate entropy
common spatial pattern
vote-based classification method
