Robust Volterra Filter Design for Enhancement of Electroencephalogram Signal Processing

Electroencephalogram (EEG) recordings often experience interference by different kinds of noise, including white, muscle, and baseline, severely limiting its utility. The recent research has demonstrated that discrete-time Volterra models can be successfully applied to reduce the broadband and narrowband noise. Their usefulness is mainly because of their ability to approximate to an arbitrary precision any fading memory system and their property of linearity with respect to parameters, the kernels coefficients. The main drawback of these models is their parametric complexity implying the need to estimate a huge number of parameters. Numerical results show that the developed algorithm achieves performance improvement over the standard filtered algorithm. This paper presents a Volterra filter (VF) algorithm based on a multichannel structure for noise reduction. Several methods have been developed, but the VF appears to be the most effective for reducing muscle and baseline noise, especially when the contamination is greater in amplitude than the brain signal. The present study introduces a new method of reducing noise in EEG signals in one step with low EEG distortion and high noise reduction. Applications with different real and synthetic signals are discussed, showing the validity of the proposed method.