联合稀疏表示的双次诱发电位提取算法

诱发电位少次提取对于研究大脑活动规律以及临床诊断等具有重要意义.根据脑电信号的特点，本文提出一种基于联合稀疏表示的双次诱发电位信号估计算法.利用诱发电位信号的准周期性和自发脑电信号的随机性，该算法将脑电信号看作为相似成分和相异成分的叠加.神经系统通过相同刺激产生的诱发电位主要在潜伏期和波幅两方面发生变化，因此该算法利用平均诱发电位进行建模，得到稀疏字典，通过联合稀疏表示算法实现双次诱发电位信号的提取.实验结果表明，该算法和其他算法相比获得了更好的效果.

Double-Trial Extraction of Evoked Potentials with Joint Sparse Representation

The few-trial extraction of evoked potentials is very meaningful to the study of brain and many clinical applications.According to the characteristics of Electroencephalogram signal,this paper presents a novel algorithm for double-trial extracting evoked potentials based on joint sparse representation.Taking advantage of the quasi-periodic structure of evoked potentials and randomness of ongoing spontaneous Electroencephalogram,the observations of evoked potentials are considered as the superposition of the similar components and the different components.Evoked potential obtained by same stimulation of the nerves changes only in latency and scale parameters.Our method uses the average evoked potentials to model and construct the sparse dictionary,so the double-trial extraction of evoked potentials can be achieved with joint sparse representation.Experiment results show that the performance of the proposed method is better than that of other methods.