Denoising of interictal EEG signals using ICA and Time Varying AR modeling

Abstract

Epilepsy is a brain disorder that 1% of people population are suffering from. One of the proper non-invasive equipment for diagnosis and analysis of this disease is electroencephalogram (EEG) recordings. However, EEG signals are often contaminated with noises and artifacts that hide epileptic signals of interest. Independent Component Analysis (ICA) is a common Blind Source Separation (BSS) method to denoise EEG signals. ICA has been proved as a worthwhile method to separate the signals of interest from noise and artifacts; nevertheless, it also has some weaknesses. In this work, to improve ICA performance in denoising context, we present an algorithm based on combination of ICA and Time Varying AutoRegressive (TVAR) model for denoising of interictal EEG signals. TVAR model is used serially after ICA method for interictal spike enhancement. The coefficients of TVAR model are estimated using Kaiman filter. The results indicate the proposed algorithm is better than ICA in terms of performance for very low Signal-to-Noise Ratio (SNR) values.