A Novel Constrained Topographic Independent Component Analysis for Separation of Epileptic Seizure Signals

Jing, Min; Sanei, Saeid

doi:https://doi.org/10.1155/2007/21315

Computational Intelligence and Neuroscience

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EEG/MEG Signal Processing

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Research Article | Open Access

Volume 2007 | Article ID 021315 | https://doi.org/10.1155/2007/21315

A Novel Constrained Topographic Independent Component Analysis for Separation of Epileptic Seizure Signals

Min Jing¹and Saeid Sanei¹

Academic Editor: Andrzej Cichocki

Received30 Dec 2006

Accepted27 May 2007

Published12 Jul 2007

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.

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Copyright

Copyright © 2007 Min Jing and Saeid Sanei. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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