一种运动想象脑电信号半监督算法

针对脑机接口中两类左右手运动想象任务，提出一种依据迭代训练次数不断更新训练集样本的半监督算法.使用多重自相关分析算法，选择置信度较高的脑电信号样本并加入初始训练集；分别使用公共平均参考法与共空域模式算法，对样本进行预处理及特征提取；最后使用支持向量机对样本进行测试，根据迭代学习次数按照递进方式移除置信度较低的样本，并利用剩余样本继续训练模型，从而优化特征提取器及分类器参数.通过对标准脑电竞赛数据库BCI IV Dataset Ⅱa中左右手运动想象数据的测试表明，该算法与其他算法相比，在训练样本数目较少的情况下，具有较高的分类正确率. 

A Semi-Supervised Classification Method for Motor Imagery EEG

A sequential updating semi-supervised classification based on training iterations was proposed for the two-class motor imagery task in brain-computer interface.Firstly,making use of multiple autocorrelation analysis,the training samples with high confidence were selected as initial training set.Then common average reference and common spatial pattern were used for pre-processing and feature extraction,respectively.Lastly,support vector machine was applied to test new samples.The samples with low confidence were removed successively according to the iterations.The remains were used to retrain the model to optimize the parameters of both feature extractor and classifier.The proposed method was applied to Dataset Ⅱa of BCI Competition IV to verify its validity.The results show that the classification accuracy is higher than other algorithms on the occasion where training samples are not enough.It can provide a new solution for the real-time BCIs.