快速准确地对脑电信号进行特征分类是脑-机接口研究的关键问题之一。从人脑决策模型出发，结合自适应小波基特征提取方法，提出了一种基于序贯似然比检验的运动想象脑电信号动态分类方法。该方法在分类中无须预先固定样本量，而是逐次取样，累积分类信息，有利于解决脑-机接口的实时控制问题。为了更好地衡量该方法的有效性，进行了10次10折交叉验证，实验结果表明3个运动想象数据集共8位受试者的平均正确率达到87％以上，互信息和分类时间等指标也表明该方法能够有效提高脑-机接口系统的性能，具有较好的实用性。%To extract and classify the electroencephalogram (EEG) signal features fast and accurately is a key issue for the brain-computer interface (BCI) systems .Based on the human decision-making model ,a motor imagery EEG dynamic classification method is proposed based on the sequential probability ratio testing (SPRT ) combined with an adaptive wavelet feature extraction method . Without pre-fixed sample size , this classification method samples and accumulates classification information successively .It is helpful to solve the real-time control problems in the BCI .In order to evaluate the effectiveness of the algorithm , a 10 times 10-fold cross-validation is used . T he experimental results show that the average classification accuracy of three motor imagery datasets of eight subjects is above 87% .The results of mutual information and classification time also show that the method can effectively improve the performance of BCI system and has good practicability .
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