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

Min Jing,Saeid Sanei

doi:10.1155/2007/21315

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.

Highlights

Epilepsy is the most common brain disorder only second to stroke, which affects nearly 60 million people in the world [1]
Independent component analysis (ICA) has been increasingly applied to brain signal analysis for decomposition of multivariate EEGs to extract the desired sources. It has found a fruitful application in the analysis of multichannel EEGs [9] including epileptic seizure signals
The applications include the implementation of joint approximate diagonalization of eigenmatrices (JADE) and fastICA for seizure detection [10, 11], artifact rejection from epileptic intracranial EEGs by minimization of mutual information [12] and spatial filtering [13], and tracking of the epileptiform activity by incorporating the spatial constraint within the fastICA [14]

Summary

Introduction

Epilepsy is the most common brain disorder only second to stroke, which affects nearly 60 million people in the world [1]. The most popular methods are based on time-frequency analysis [7] and artificial neural networks [8] These methods do not exploit the multichannel electroencephalogram (EEG) information effectively. Independent component analysis (ICA) has been increasingly applied to brain signal analysis for decomposition of multivariate EEGs to extract the desired sources. It has found a fruitful application in the analysis of multichannel EEGs [9] including epileptic seizure signals. The traditional nonlinear analysis methods can be applied to these seizure components for investigation of predictability This approach can be further improved if a better performance of separation can be achieved.

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Conclusion