title: A joint sparse representation-based method for double-trial evoked potentials estimation.
abstract: In this paper, we present a novel approach to solving an evoked potentials estimating problem. Generally, the evoked potentials in two consecutive trials obtained by repeated identical stimuli of the nerves are extremely similar. In order to trace evoked potentials, we propose a joint sparse representation-based double-trial evoked potentials estimation method, taking full advantage of this similarity. The estimation process is performed in three stages: first, according to the similarity of evoked potentials and the randomness of a spontaneous electroencephalogram, the two consecutive observations of evoked potentials are considered as superpositions of the common component and the unique components; second, making use of their characteristics, the two sparse dictionaries are constructed; and finally, we apply the joint sparse representation method in order to extract the common component of double-trial observations, instead of the evoked potential in each trial. A series of experiments carried out on simulated and human test responses confirmed the superior performance of our method.
authors.affiliation: School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China.
authors.forename: Nannan
authors.initials: N
authors.lastname: Yu
authors.forename: Haikuan
authors.initials: H
authors.lastname: Liu
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Wang
authors.forename: Hanbing
authors.initials: H
authors.lastname: Lu
mesh_terms: D004562:Electrocardiography; D001330:Electronic Data Processing; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D008954:Models, Biological
keywords: Dictionary constructing; Double-trial evoked potentials estimation; Joint sparse representation; Spontaneous electroencephalogram