【摘要】 对诱发电位(EP)信号中具有强脉冲过程的脑电图(EEG)噪声,可以用α稳定分布模型来描述。基于分数低阶矩对传统的Cohen类时频分布进行了改进,得到了新的分数低阶空间时频分布(FLO-STFM),据此提出了一种新的可在α稳定分布环境下工作的分数低阶空间时频欠定盲分离算法(FLO-TF-UBSS)。将该盲分离算法应用到EP信号的提取,仿真实验结果表明所提出的盲分离算法能较好地在EEG噪声环境下实现对EP信号的盲提取,相关系数以及盲提取效果都优于基于二阶的TF-UBSS算法。更多还原

【Abstract】 The impulsive electroencephalograph(EEG)noises in evoked potential(EP)signals is very strong,usually with a heavy tail and infinite variance characteristics like the acceleration noise impact,hypoxia and etc.,as shown in other special tests.The noises can be described byαstable distribution model.In this paper,Wigner-Ville distribution(WVD)and pseudo Wigner-Ville distribution(PWVD)time-frequency distribution based on the fractional lower order moment are presented to be improved.We got fractional lower order WVD(FLO-WVD)and fractional lower order PWVD(FLO-PWVD)time-frequency distribution which could be suitable forαstable distribution process.We also proposed the fractional lower order spatial time-frequency distribution matrix(FLO-STFM)concept.Therefore,combining with time-frequency underdetermined blind source separation(TF-UBSS),we proposed a new fractional lower order spatial time-frequency underdetermined blind source separation(FLO-TF-UBSS)which can work inαstable distribution environment.We used the FLO-TF-UBSS algorithm to extract EPs.Simulations showed that the proposed method could effectively extract EPs in EEG noises,and the separated EPs and EEG signals based on FLOTF-UBSS were almost the same as the original signal,but blind separation based on TF-UBSS had certain deviation.The correlation coefficient of the FLO-TF-UBSS algorithm was higher than the TF-UBSS algorithm when generalized signal-to-noise ratio(GSNR)changed from 10 dB to 30 dB andαvaried from 1.06 to 1.94,and was approximately equal to 1.Hence,the proposed FLO-TF-UBSS method might be better than the TF-UBSS algorithm based on second order for extracting EP signal under an EEG noise environment.更多还原