A Novel constrained Topographic ICA for separation of epileptic seizure signals

Min Jing, Saeid Sanei

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Blind separation of the electroencephalogram signals (EEGs) using topographic independent component analysis (TICA) is an effective tool to group the geometrically nearby source signals. The TICA algorithm further improves the results if the desired signal sources have particular properties which can be exploited in the separation process as constraints. Here, the spatial-frequency information of the seizure signals is used to design a constrained TICA for the separation of epileptic seizure signal sources from the multichannel EEGs. The performance is compared with those from the TICA and other conventional ICA algorithms. The superiority of the new constrained TICA has been validated in terms of signal-to-interference ratio and correlation measurement.
LanguageEnglish
Pages15-22
JournalComputational Intelligence and Neuroscience
Volume2007
DOIs
Publication statusPublished - 2007

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Independent component analysis
Electroencephalography

Cite this

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title = "A Novel constrained Topographic ICA for separation of epileptic seizure signals",
abstract = "Blind separation of the electroencephalogram signals (EEGs) using topographic independent component analysis (TICA) is an effective tool to group the geometrically nearby source signals. The TICA algorithm further improves the results if the desired signal sources have particular properties which can be exploited in the separation process as constraints. Here, the spatial-frequency information of the seizure signals is used to design a constrained TICA for the separation of epileptic seizure signal sources from the multichannel EEGs. The performance is compared with those from the TICA and other conventional ICA algorithms. The superiority of the new constrained TICA has been validated in terms of signal-to-interference ratio and correlation measurement.",
author = "Min Jing and Saeid Sanei",
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A Novel constrained Topographic ICA for separation of epileptic seizure signals. / Jing, Min; Sanei, Saeid.

In: Computational Intelligence and Neuroscience, Vol. 2007, 2007, p. 15-22.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A Novel constrained Topographic ICA for separation of epileptic seizure signals

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AU - Sanei, Saeid

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AB - Blind separation of the electroencephalogram signals (EEGs) using topographic independent component analysis (TICA) is an effective tool to group the geometrically nearby source signals. The TICA algorithm further improves the results if the desired signal sources have particular properties which can be exploited in the separation process as constraints. Here, the spatial-frequency information of the seizure signals is used to design a constrained TICA for the separation of epileptic seizure signal sources from the multichannel EEGs. The performance is compared with those from the TICA and other conventional ICA algorithms. The superiority of the new constrained TICA has been validated in terms of signal-to-interference ratio and correlation measurement.

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JO - Computational Intelligence and Neuroscience

T2 - Computational Intelligence and Neuroscience

JF - Computational Intelligence and Neuroscience

SN - 1687-5265

ER -