Evoked Potentials Estimation by using Higher Order Adaptive Neural Network filter

Evoked potentials are usually embedded in the ongoing electroencephalogram with a very low signal-to-noise ratio. The neural network filtering technique which has the advantage of complex mapping is one of the applicable methods for evoked potentials estimation. The backpropagation algorithm based o...

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Veröffentlicht in:Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Jg. 2005; S. 1139 - 1141
Hauptverfasser: Lin, Bor-Shyh, Lin, Bor-Shing, Chong, Fok-Ching
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 2005
Schlagworte:
Adaptive filters
Adaptive systems
Artificial neural networks
back-propagation algorithm
Biomedical engineering
Brain modeling
Cost function
Electroencephalography
evoked potentials
Higher order statistics
neural network filter
Neural networks
Roentgenium
ISBN:0780387414, 9780780387416
ISSN:1094-687X, 1557-170X
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Zusammenfassung:Evoked potentials are usually embedded in the ongoing electroencephalogram with a very low signal-to-noise ratio. The neural network filtering technique which has the advantage of complex mapping is one of the applicable methods for evoked potentials estimation. The backpropagation algorithm based on second order statistics is commonly used to adapt neural network filters. However it is easily influenced by additive Gaussian noise. In this study, a neural network filter with a modified back-propagation algorithm for higher order statistics was proposed. With higher-order statistics technique, additive Gaussian noise is suppressed to improve the performance of evoked potentials estimation
Bibliographie:ObjectType-Article-1
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content type line 23
ISBN:0780387414
9780780387416
ISSN:1094-687X
1557-170X
DOI:10.1109/IEMBS.2005.1616622