电子测量与仪器学报

陈妮,范泽平,曹欣燃,覃玉荣.基于 AVMD 的非线性经颅电刺激伪迹去除方法[J].电子测量与仪器学报,2022,36(6):30-41

基于 AVMD 的非线性经颅电刺激伪迹去除方法

Artifact removal of nonlinear transcranial electrical stimulationsusing adaptive variational mode decomposition

DOI：

英文关键词:transcranial alternating current stimulation nonlinear artifact variational mode decomposition EEG

基金项目:广西自然科学基金（2016GXNSFAA380068）项目资助

作者	单位
陈妮	1. 广西大学电气工程学院，2. 广西医科大学基础医学院
范泽平	3. 广西大学计算机与电子信息学院
曹欣燃	3. 广西大学计算机与电子信息学院
覃玉荣	3. 广西大学计算机与电子信息学院

Author	Institution
Chen Ni	1. College of Electrical Engineering, Guangxi University,2. Department of Biomedical Engineering, Guangxi Medical University
Fan Zeping	3. College of Computer and Electronic Information, Guangxi University
Cao Xinran	3. College of Computer and Electronic Information, Guangxi University
Qin Yurong	3. College of Computer and Electronic Information, Guangxi University

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中文摘要:

经颅交流电刺激( transcranial alternating current stimulation, tACS) 是一种应用广泛的无创脑刺激方法。由于非线性 tACS 伪迹的干扰,很难直接获取刺激时神经电活动的真实情况。为此,提出一种自适应变分模式分解( adaptive variational mode decomposition, AVMD)方法用于去除非线性 tACS 伪迹。该方法利用希尔伯特变换(Hilbert transform, HT)提取伪迹包络,然后利用窗口傅里叶变换(window Fourier transform, WFT)确定 VMD 分解的模态数。再利用 VMD 分解原始数据得到多个本征模态信号。最后根据各模态信号的幅度特征重构真实脑电成分。在模拟数据和公开实验数据上测试 AVMD 方法的性能,分别采用重构脑电与真实脑电之间的相关系数(模拟数据)以及重构脑电和 sham 脑电统计特征的平均绝对误差(实验数据)进行方法性能评价。结果表明,对于模拟数据,在调幅深度 ma∈[0. 001,0. 01]、相位调制深度 mp∈[0. 001,0. 01]和刺激频率 f arti∈[10, 100]的条件下,重构脑电和真实脑电的平均相关系数分别为 0. 988 5、0. 893 5 和 0. 948 4。对于实验数据,重构脑电和 sham 脑电之间统计特征的平均绝对误差在刺激频率为 11 Hz 时分别为 0. 989 6(峰度)、2. 991 8(均方根幅度)、0. 175 1(样本熵),在刺激频率为 62 Hz 时为 0. 940 7(峰度)、2. 473 1(均方根幅度)和 0. 084 1(样本熵)。与移动叠加平均法( superposition of moving averages, SMA)、自适应滤波法(adaptive filtering, AF)和经验模态分解法(empirical mode decomposition, EMD)相比,AVMD 方法表现出更稳定更好的非线性 tACS 伪迹去除性能。该方法的提出为闭环 tACS 刺激仪器的开发提供支持。

英文摘要:

Transcranial alternating current stimulation (tACS) is a widely used noninvasive brain stimulation method. However, due to nonlinear electrical stimulation artifacts interference, it is difficult to obtain the real neural activity during stimulation directly. Therefore, an adaptive variational mode decomposition (AVMD) method is proposed to remove the nonlinear tACS artifacts. In this method, the envelope of artifacts is extracted by Hilbert transform (HT), then, the VMD modes is obtained by WFT spectrum analysis. VMD is used to decompose the recorded data to obtain multiple intrinsic mode signals. According to the amplitude characteristics, the artifact components are selected, and the effective EEG components are recovered. AVMD algorithm were tested on the synthetic data and the public experimental data. The correlation coefficient between reconstructed EEG and real EEG was used to measure the artifact removal effect for the synthetic data. The mean absolute error (MAE) of the statistical characteristics between recovered EEG and sham EEG was used to evaluate the artifact removal effect for the experimental data. For the synthetic data, under the conditions of amplitude modulation depth ma∈ [0. 001, 0. 01], phase modulation depth mp∈ [0. 001, 0. 01] and stimulation frequency f arti∈ [10, 100],the average correlation coefficients between reconstructed EEG and real EEG are 0. 988 5, 0. 893 5, 0. 948 4, respectively. The MAE of the statistical characteristics between recovered EEG and sham EEG are 0. 989 6 ( kurtosis), 2. 991 8( root mean square amplitude), 0. 175 1 (sample entropy) for the experimental data with the stimulation frequency 11 Hz, and are 0. 940 7 (kurtosis), 2. 473 1 (root mean square amplitude) and 0. 084 1 (sample entropy) for the experimental data with the stimulation frequency 62 Hz. AVMD method shows more stable and better nonlinear tACS artifact removal performance compared with superposition of moving averages ( SMA), adaptive filtering (AF) and empirical mode decomposition (EMD). This method provides support for the development of closed-loop tACS instrument.

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