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Computational Intelligence and Neuroscience
Volume 2007 (2007), Article ID 21315, 7 pages
Research Article

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

Centre of Digital Signal Processing, Cardiff University, Cardiff CF24 3AA, Wales, UK

Received 30 December 2006; Accepted 27 May 2007

Academic Editor: Andrzej Cichocki

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.


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.