Combining Nonlinear Dimensionality Reduction with Wavelet Network to Solve EEG Inverse Problem

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