Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IEMBS.2006.259217 | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference |
Keywords | DocType | Volume |
improved isometric mapping algorithm,electroencephalography signal,radial basis function networks,eeg inverse problem,nonlinear dimensionality reduction,neurophysiology,wavelet transforms,single-scaling radial-basis wavelet network module,electroencephalography,real-time localizer,medical computing,integrated multimethod system,eeg source model,neuroelectric source parameter analysis,inverse problem,simulation experiment,real time | Conference | 1 |
Issue | ISSN | ISBN |
null | 1557-170X | 1-4244-003303 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
6 | ||