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Authors: J. Mateo 1 ; A. Torres 1 ; C. Soria 2 ; Mª. García 2 and C. Sánchez 1

Affiliations: 1 University of Castilla-La Mancha, Spain ; 2 Virgen de la Luz Hospital (SESCAM), Spain

ISBN: 978-989-8425-84-3

Keyword(s): Biomedical signals, Muscle noise, Electrocardiogram, Neural network.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Domain Applications and Case Studies ; Enterprise Information Systems ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Electroencephalogram (EEG) recordings often experience interference by different kinds of noise, including white and muscle, severely limiting its utility. Artificial neural networks (ANNs) are effective and powerful tools for removing interference from EEGs, but the quality of the separation is highly dependent on the type and degree of contamination. Several methods have been developed, but ANNs appear to be the most effective for reducing muscle contamination, especially when the contamination is greater in amplitude than the brain signal. We propose an ANN as a filter for EEG recordings, developing a novel framework for investigating and comparing the relative performance of an ANN incorporating real EEG recordings from the Clinical Neurophysiology Service at the Virgen de la Luz Hospital in Cuenca (Spain). This method was based on a growing ANN that optimised the number of nodes in the hidden layer and the coefficient matrices, which were optimised by the simultaneous perturbatio n method. The ANN improved the results obtained with the conventional EEG filtering techniques: wavelet, singular value decomposition, principal component analysis, adaptive filtering and independent components analysis. The system was evaluated within a wide range of EEG signals in which noise was added. The present study introduces a method of reducing all EEG interference signals with low EEG distortion and high noise reduction. (More)

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Paper citation in several formats:
Mateo, J.; Torres, A.; Soria, C.; García, M. and Sánchez, C. (2011). EEG NOISE CANCELLATION BASED ON NEURAL NETWORK.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 330-333. DOI: 10.5220/0003657103300333

@conference{ncta11,
author={J. Mateo. and A. Torres. and C. Soria. and Mª. García. and C. Sánchez.},
title={EEG NOISE CANCELLATION BASED ON NEURAL NETWORK},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={330-333},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003657103300333},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - EEG NOISE CANCELLATION BASED ON NEURAL NETWORK
SN - 978-989-8425-84-3
AU - Mateo, J.
AU - Torres, A.
AU - Soria, C.
AU - García, M.
AU - Sánchez, C.
PY - 2011
SP - 330
EP - 333
DO - 10.5220/0003657103300333

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