title: Combining nonlinear dimensionality reduction with wavelet network to solve EEG inverse problem.
abstract: An integrated multi-method system to analyze the neuroelectric source parameters of electroencephalography (EEG) signal is presented. In order to handle the large-scale high dimension data efficiently and provide a real-time localizer in EEG inverse problem, an improved isometric mapping algorithm is used to find the low dimensional manifolds from high dimensional recorded EEG. Then, based on reduced dimension data, a single-scaling radial-basis wavelet network module is employed to determine the parameters of different type of EEG source models. In our simulation experiments, satisfactory results are obtained.
authors.affiliation: Coll. of Comput. Sci. & Software, Hebei Univ. of Technol., Tianjin. qingwu@hebut.edu.cn
authors.forename: Qing
authors.initials: Q
authors.lastname: Wu
authors.forename: Lukui
authors.initials: L
authors.lastname: Shi
authors.forename: Youxi
authors.initials: Y
authors.lastname: Wu
authors.forename: Guizhi
authors.initials: G
authors.lastname: Xu
authors.forename: Ying
authors.initials: Y
authors.lastname: Li
authors.forename: Weili
authors.initials: W
authors.lastname: Yan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods; D005071:Evoked Potentials; D006801:Humans; D016018:Least-Squares Analysis; D015225:Magnetoencephalography; D008568:Memory / Q000502:physiology; D016571:Neural Networks, Computer