Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source Separation


Por: Romero, S, Mananas, MA, Barbanoj, MJ

Publicada: 1 ene 2009
Resumen:
Quantitative electroencephalographic (EEG) analysis is very useful for diagnosing dysfunctional neural states and for evaluating drug effects on the brain, among others. However, the bidirectional contamination between electrooculographic (EOG) and cerebral activities can mislead and induce wrong conclusions from EEG recordings. Different methods for ocular reduction have been developed but only few studies have shown an objective evaluation of their performance. For this purpose, the following approaches were evaluated with simulated data: regression analysis, adaptive filtering, and blind source separation (BSS). In the first two, filtered versions were also taken into account by filtering EOG references in order to reduce the cancellation of cerebral high frequency components in EEG data. Performance of these methods was quantitatively evaluated by level of similarity, agreement and errors in spectral variables both between sources and corrected EEG recordings. Topographic distributions showed that errors were located at anterior sites and especially in frontopolar and lateral-frontal regions. In addition, these errors were higher in theta and especially delta band. In general, filtered versions of time-domain regression and of adaptive filtering with RLS algorithm provided a very effective ocular reduction. However, BSS based on second order statistics showed the highest similarity indexes and the lowest errors in spectral variables.

Filiaciones:
Romero, S:
 Univ Politecn Cataluna, Biomed Engn Res Ctr, Dept Automat Control ESAII, Barcelona, Spain

 CIBER Bioingn Biomat & Nanomed CIBER BBN, Barcelona, Spain

Mananas, MA:
 Univ Politecn Cataluna, Biomed Engn Res Ctr, Dept Automat Control ESAII, Barcelona, Spain

 CIBER Bioingn Biomat & Nanomed CIBER BBN, Barcelona, Spain

Barbanoj, MJ:
 Univ Autonoma Barcelona, Dept Pharmacol & Therapeut, St Pau Hosp, Res Inst,Drug Res Ctr CIM, E-08193 Barcelona, Spain
ISSN: 00906964





ANNALS OF BIOMEDICAL ENGINEERING
Editorial
SPRINGER, ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES, Estados Unidos America
Tipo de documento: Article
Volumen: 37 Número: 1
Páginas: 176-191
WOS Id: 000261401100014
ID de PubMed: 18985453
imagen Green Published

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