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Automatic Selection of Frequency Bands for Electroencephalographic Source Localization

Munoz, Pablo; Giraldo, Eduardo; Lopez, Maximiliano Bueno; Molinas Cabrera, Maria Marta
Journal article, Peer reviewed
Accepted version
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URI
http://hdl.handle.net/11250/2639383
Date
2019
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  • Institutt for teknisk kybernetikk [2193]
  • Publikasjoner fra CRIStin - NTNU [19946]
Original version
International IEEE/EMBS Conference on Neural Engineering. 2019, 2019-March 1179-1182.   10.1109/NER.2019.8716979
Abstract
This paper shows a method to locate actives sources from pre-processed electroencephalographic signals. These signals are processed using multivariate empirical mode decomposition (MEMD). The intrinsic mode functions are analyzed through the Hilbert-Huang spectral entropy. A cost function is proposed to automatically select the intrinsic mode functions associated with the lowest spectral entropy values and they are used to reconstruct the neural activity generated by the active sources. Multiple sparse priors are used to locate the active sources with and without multivariate empirical mode decomposition and the performance is estimated using the Wasserstein metric. The results were obtained for conditions with high noise (Signal-to-Noise-Ratio of -5dB), where the estimated location, for five sources, was better for multiple sparse prior with Multivariate Empirical Mode Decomposition, and with low noise (Signal-to-Noise-Ratio of 20dB), where the estimated location, for three sources, was better for multiple sparse prior without MEMD.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Journal
International IEEE/EMBS Conference on Neural Engineering

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