电子测量与仪器学报

陈妮,覃玉荣,孙鹏飞.基于脑电自回归预测的实时相位估计方法[J].电子测量与仪器学报,2020,34(6):183-190

基于脑电自回归预测的实时相位估计方法

Real time phase estimation method based on autoregressive prediction of EEG

DOI：

英文关键词:autoregressive model EEG phase estimation phase locked TES

基金项目:广西自然科学基金(2016GXNSFAA380068)资助项目

作者	单位
陈妮	1. 广西大学电气工程学院，2. 广西医科大学生物医学工程系
覃玉荣	3. 广西大学计算机与电子信息学院
孙鹏飞	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
Qin Yurong	3. School of Computer and Electronic Information, Guangxi University
Sun Pengfei	3. School of Computer and Electronic Information, Guangxi University

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中文摘要:

经颅电刺激(transcranial electric stimulation, TES)等无创刺激方式在与大脑内在神经电活动锁相时,能更有效的调节神经振荡活动。由于脑电(electroencephalogram, EEG)信号复杂的时变性,现有的方法难以同时满足相位估计精度和实时性的要求。为此,提出一种用于锁相刺激系统的实时相位估计方法。该方法对 EEG 信号进行自回归(autoregressive, AR)建模,然后利用 AR 模型预测 EEG 信号并进行相位特征点识别,再通过相位特征点计算出待刺激点的相位。采用该方法对 20 名受试者(年龄 20~ 36 岁,男性 12 名,女性 8 名)的闭眼静息 EEG 数据进行分析,发现该方法的性能与模型系数更新时长、预测步长及 EEG 的窄带功率大小有关,对高窄带功率的 EEG 数据具有更优性能;在最佳模型参数下(模型系数更新时长为 5 s、预测步长为 30), 20 名受试者的平均锁相指数(phase locking value,PLV)为 0. 968,平均相位误差(average phase error,APE)为 13. 33°。相对于平均周期法,该方法具有更高的 PLV 值和更低的相位误差,可用于闭环锁相经颅电刺激仪器的研发。

英文摘要:

When the non-invasive stimulation such as transcranial electric stimulation locks phases with the intrinsic neural electrical activity in the brain, the neural oscillatory activity can be regulated in a more effective manner. Due to the complex time-variation of EEG signal, the existing methods cannot meet the accuracy of phase estimation and real-time performance of the system at the same time. In this paper, a real-time phase estimation method for phase-locked stimulus system was proposed. In this method, the EEG signal was modeled by autoregressive (AR), then the AR model was used to predict the EEG signal and identify the phase feature points, and the phase to be stimulated was calculated by the phase feature points. The method was used to analyze the closed eye resting EEG of 20 subjects (aged 20~ 36, male 12, female 8) and it was found that the performance of the method is related to the updating time of the model coefficient, the prediction step and the narrow-band power of the EEG. It had better performance for the EEG with higher narrowband power. Under the optimal model parameters (the updating time of the model coefficient was 5 s and the predicted step length was 30), the average phase locking value (PLV) of the 20 subjects was 0. 968, and the average phase error was 13. 33. Compared with the average period method, this method has higher PLV value and lower phase error, which can be used in the development of closed-loop phase-locked electric stimulator.

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