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- W2610195026 abstract "Two techniques are proposed to alleviate the computational burden of MUltiple SIgnal Classification (MUSIC) algorithm applied to Electroencephalogram (EEG) source localization. A significant reduction was achieved by parsing the cortex surface into smaller regions and nominating only a few regions for the exhaustive search inherent in the MUSIC algorithm. The nomination procedure involves a dictionary learning phase in which each region is assigned an atom matrix. Moreover, a dimensionality reduction step provided by excluding some of the electrodes is designed such that the Cramer-Rao bound of localization is maintained. It is shown by simulation that computational complexity of the MUSIC-based localization can be reduced up to 80%." @default.
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- W2610195026 date "2016-12-01" @default.
- W2610195026 modified "2023-09-27" @default.
- W2610195026 title "Complexity reduction techniques in music-based EEG source localization" @default.
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- W2610195026 doi "https://doi.org/10.1109/globalsip.2016.7906018" @default.
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