ID 原文 译文
40426 分别采用支持向量机(Support vector machine，SVM)、K近邻(K-nearest neighbor，KNN)和线性判别分析(Linear discriminant analysis，LDA)3种分类器，对6种头部动作(低头、抬头、左摆头、右摆头、左转头和右转头)的MMG信号进行分类。 The MMG of six head movements(forward, backward, swing to left, swing to right, turn to light, turn to right)are classified by adopting three sorts of classifiers, which are support vector machine(SVM), K nearest neighbor(KNN) and linear discriminant analysis(LDA).
40427 实验结果表明，选用时域、时-频域和非线性动力学特征组合的方式，以及使用SVM作为分类器，可使各类动作的分类精度均达到80%以上，从而获得相对较高的准确率。 Experimental results show that selecting the method of combining features in time domain, time-frequency and non-linear dynamic, and adopting SVM as the classifier can improve the classification accuracy up to higher than 80% in each movement, thus acquiring relatively higher rate.
40428 针对微弱的脑电(Electroencephalogram，EEG)信号在采集过程中夹杂着各种生理伪迹，特别易遭到眨眼和眼动产生的眼电(Electrooculography，EOG)伪迹干扰。 Due to the weak electroencephalogram(EEG)signal during the acquisition process, the EEG is mixed with various physiological artifacts, so it is particularly susceptible to electrooculography(EOG)interference caused by eye blinking and eye movement.
40429 本文提出在自适应噪声完备经验模态分解(Complete ensemble empirical mode decomposition with adaptive noise，CEEMDAN)的基础上，构建盲反卷积(Blind deconvolution，BD)模型，实现EOG伪迹分离的方法。 A method for constructing a blind deconvolution(BD) model based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)is proposed to achieve EOG artifact separation.
40430 该方法首先运用CEEMDAN方法将含有伪迹的EEG信号分解成若干固有模态函数(Intrinsic mode function，IMF)分量， Firstly, the CEEMDAN method is used to decompose the EEG signal containing artifacts into several intrinsic mode functions(IMF).
40431 再以模态分量为观测信号送入EEG信号和EOG伪迹两个源信号构成的盲反卷积模型中， Secondly, the modal component is used as the observation signal to send the EEG signal and the EOG artifacts to form a BD model.
40432 通过构建代价函数迭代实现EEG信号与EOG伪迹分离。 Finally, the separation of EEG signal and EOG artifacts is realized by constructing the cost function iteratively.
40433 为了验证新提出的算法，采用标准CHB-MIT头皮脑电数据库进行实验验证， To verify the proposed algorithm, the standard Children's Hospital Boston(CHB)and the Massachusetts Institute of Technology(MIT)(CHB-MIT)scalp EEG database is used for experimental verification.
40434 EOG伪迹分离后的数据跟原始脑电数据作相关性分析，其相关系数是0.82。 The correlation between the EOG artifact separation data and the original EEG data is analyzed, and the correlation coefficient is 0.82.
40435 结果证实本文提出的方法保留有大多数原始EEG信号分量，同时对EOG伪迹的分离也具有良好的效果。 The results confirm that this method retains most of the original EEG signal components and has a good effect on the separation of EOG artifacts.