基于自适应动态特征库的P300诱发脑电单次提取算法研究

摘要

目的 诱发电位的单次提取技术一直是脑电信息处理领域的难题之一,为进一步提高单次提取算法的时间准确性和特征精度,针对体感诱发脑电数据信噪比低、试次间参数变化大的特点,研究诱发脑电参数单次提取新算法,保留试次间诱发脑电的动态特性,并提高估计准确率.方法 基于小波滤波和多元线性分析技术,加入自适应动态特征库并由此提出的诱发脑电P300参数单次提取新方法.随机选取4组小波滤波(WF)后诱发脑电数据,分别叠加平均后进行主成分分析(PCA)组成特征库.单次提取时,针对每试次数据从特征库中选择与当次诱发脑电信号相关系数最高的成分作为自变量开展多元线性回归分析,由回归分析结构重构出单次诱发电位信号并自动提取潜伏期和幅值等关键特征.结果 与专家判定的基准数值相比,新算法预测的P300成分潜伏期与幅值参数更准确,两者的平均差值分别为(11.16±8.60) ms和(1.40±1.34)μV;与常用的叠加平均法结果亦更为接近,平均差值分别为(23.26±25.76) ms和(2.52±2.50) μV,新算法相比传统多元线性回归分析算法具有显著优势.结论 将动态更新的诱发脑电数据主成分样本库应用于小波滤波与多元线性回归方法,能有效保留单次诱发脑电数据中的动态特征,从而提升参数估计的准确率.%Objective The single-trial extraction method of evoked potential has been one of the problems in EEG information processing field.According to the characteristics of somatosensory evoked electroencephalogram (EEG) with low signal-to-noise ratio and large parameter variation between trials,a novel single-trial extraction method for evoked potentials was proposed.This method aims to further improve the accuracy and characteristics of the single-trial extraction algorithm,preserve more dynamic characteristics between trials,and improve the estimation accuracy.Methods Based on wavelet filtering and multiple linear analysis,a new single-trial extraction method for EEG P300 parameters was proposed by applying the adaptive dynamic feature library.Four groups of wavelet filtered evoked EEG data were randomly selected,and used to build the feature library using overlapping average method and principal component analysis.For the single-trial extracted EEG data,the component with the highest correlation coefficient related with the current data was selected as the independent variable from the feature library,and the relevant multiple linear regression analysis was conducted.The single-trial evoked potential signal was reconstructed by the regression analysis results,in which the key features such as latency and amplitude were automatically extracted.Results Compared with the benchmark values determined by experts,the proposed algorithn can obtain more accurate estimation values of latency and amplitude in P300 components.The average difference of latency and amplitude by the proposed algorithm is (11.16±8.60) ms and (1.40±1.34)μV,respectively.These two values obtained by the proposed algorithm are much closer to that obtained by the commonly used overlapping average method of (23.26±25.76) ms and (2.52±2.50) μV,respectively.These results show that the proposed algorithm has significant advantages comparing with the traditional multiple linear regression analysis algorithm.Conclusions The dynamic updating principal component sample library of EEG data was applied to wavelet filtering and multiple linear regression,thus the dynamic characteristics were effectively preserved,and the accuracy of parameter estimation was improved.