Transient model of EEG using Gini Index-based matching pursuit

Carlos A. Loza, José C. Principe

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

10 Citas (Scopus)

Resumen

We introduce a novel, transient model for the electroencephalogram (EEG) as the noisy addition of linear filters responding to trains of delta functions. We set the synthesis part as a parameter-tuning problem and obtain synthetic EEG-like data that visually resembles brain activity in the time and frequency domains. For the analysis counterpart, we use sparse approximation to decompose the signal in relevant events via Matching Pursuit. We improve this algorithm by incorporating the Gini Index as a stopping criteria; in this way, we promote sparse sources while, at the same time, eliminating one of the free parameters of Matching Pursuit. Results are presented using synthetic EEG and BCI competition data. Statistics of the model parameters are more informative and posses finer temporal resolution than classical methods such as Power Spectral Density (PSD) estimation.

Idioma originalInglés
Título de la publicación alojada2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas724-728
Número de páginas5
ISBN (versión digital)9781479999880
DOI
EstadoPublicada - 18 may. 2016
Publicado de forma externa
Evento41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duración: 20 mar. 201625 mar. 2016

Serie de la publicación

NombreICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volumen2016-May
ISSN (versión impresa)1520-6149

Conferencia

Conferencia41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
País/TerritorioChina
CiudadShanghai
Período20/03/1625/03/16

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