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Electroencephalogram (EEG) source localization is well known as an import inverse problem of electrophysiology. In order to improve the accuracy of inverse calculation from EEG signal, a new method combining multidimensional SVR with nonlinear dimensionality reduction was proposed. In our study, the ISOMAP algorithm was firstly used to find the low dimensional manifolds from high dimensional EEG signal...
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...
Recently, there are several algorithms to perform dimensionality reduction on low-dimensional nonlinear manifolds embedded in a high-dimensional space, such as ISOMAP, LLE, Laplacian eigenmaps, SPE and so on. Most of these techniques work in batch mode. In this paper, we present an incremental nonlinear dimensionality reduction algorithm based on the k nearest neighbor projection. The method can effectively...
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...
Most of existing nonlinear dimensionality reduction algorithms, such as isomap, LEE, Laplacian Eigenmaps, SPE and so on, do not provide a simple generalization to discover the low-dimensional embedding for new data points. In this paper, we present a robust extension for isomap to efficiently map new samples into the low-dimensional space. This generalization permits one to apply a trained model to...
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