dc.contributor.author | Munoz, Pablo | |
dc.contributor.author | Giraldo, Eduardo | |
dc.contributor.author | Lopez, Maximiliano Bueno | |
dc.contributor.author | Molinas Cabrera, Maria Marta | |
dc.date.accessioned | 2020-02-03T14:38:30Z | |
dc.date.available | 2020-02-03T14:38:30Z | |
dc.date.created | 2019-07-17T13:26:56Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | International IEEE/EMBS Conference on Neural Engineering. 2019, 2019-March 1179-1182. | nb_NO |
dc.identifier.issn | 1948-3546 | |
dc.identifier.uri | http://hdl.handle.net/11250/2639383 | |
dc.description.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. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | nb_NO |
dc.title | Automatic Selection of Frequency Bands for Electroencephalographic Source Localization | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 1179-1182 | nb_NO |
dc.source.volume | 2019-March | nb_NO |
dc.source.journal | International IEEE/EMBS Conference on Neural Engineering | nb_NO |
dc.identifier.doi | 10.1109/NER.2019.8716979 | |
dc.identifier.cristin | 1711777 | |
dc.description.localcode | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | nb_NO |
cristin.unitcode | 194,63,25,0 | |
cristin.unitname | Institutt for teknisk kybernetikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |