Title

Automatic Selection of Frequency Bands for Electroencephalographic Source Localization

DOI

https://doi.org/10.1109/NER.2019.8716979

Document Type

Conference Proceeding

Publication Date

5-16-2019

Publication Title

International IEEE/EMBS Conference on Neural Engineering, NER

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.

Volume

2019-March

First Page

1179

Last Page

1182

ISSN

19483546

ISBN

9781538679210

Identifier

SCOPUS_ID:85066764617

Documento original

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