Título
Artifact elimination from EEG signals using parametric modeling restoration and independent component analysis
Autor
Luis Peraza Rodriguez
Nivel de Acceso
Acceso Abierto
Materias
Resumen o descripción
This thesis faces the problem of eliminating time-constrained artifacts from electroencephalographic (EEG) signals. Four signal restoration techniques are analyzed, autoregressive interpolation (ARI), linear prediction interpolation (LPI), warped linear
prediction interpolation (WLPI), and a novel technique proposed in this thesis, Fourier
linear combiner interpolation (FLCI). The signal restoration techniques are based on
widely accepted models for EEG signals. First, these techniques are used to remove
time-constrained artifacts from a single EEG channel when few electrodes are available, as occurs in neonatal EEG and polysomnography. Here, we prove the preserving
of the spectral information within the restored segment. Further, when having more
available electrodes and knowing that a time-constrained artifact contaminates several
channels, we propose to restore the artifactual independent component (IC) instead of
zeroing it out, which is a common practice. It is proved that in the bands of interest
the spectral information is enhanced by reducing the mean squared error along the
frequency components.
Editor
Instituto Tecnológico y de Estudios Superiores de Monterrey
Fecha de publicación
1 de mayo de 2007
Tipo de publicación
Tesis de maestría
Recurso de información
Formato
application/pdf
Idioma
Inglés
Repositorio Orígen
Repositorio Institucional del Tecnológico de Monterrey
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