A Hybrid Optimization Method OWGWA for EEG/ERP Adaptive Noise Canceller With Controlled Search Space

A Hybrid Optimization Method OWGWA for EEG/ERP Adaptive Noise Canceller With Controlled Search Space

Rachana Nagal (Amity University, Noida, India), Pradeep Kumar (Amity University, Noida, India) and Poonam Bansal (Maharaja Surajmal Institute of Technology, Delhi, India)
Copyright: © 2020 |Pages: 19
DOI: 10.4018/IJSIR.2020070103

Abstract

In this paper, a system for filtering event-related potentials/electroencephalograph is exhibited by adaptive noise canceller through an optimization algorithm, oppositional hybrid whale-grey wolf optimization algorithm (OWGWA). The OWGWA can choose the control parameters of the grey wolf algorithm utilizing whale parameters. To balance out the randomness of optimization strategies another methodology is implemented called controlled search space. Adaptive filter's noise reduction capability has been tested through adding adaptive white Gaussian noise over contaminated EEG signals at different noise levels. The performance of the proposed OWGWA-CSS algorithm is evaluated by signal to noise ratio in dB, mean value, and the relationship between resultant and input ERP. In this work, ANCs are also implemented by utilizing other optimization techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWGWA-CSS technique provides higher SNR value, significantly lower mean and higher correlation as compared to other techniques.
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Introduction

Electroencephalograph (EEG) is an electrical signal recorded from the brain and very effective to diagnose the diseases related to the brain (Sanei & Chambers, 2007). But the high-quality EEG signals are contaminated by noise. It has been noted that the presence of noise makes an accurate evaluation of EEG signals difficult (Reddy & Narava, 2013). These noises may be generated because of the wrong placement of the electrodes or because of the eyeball, hand, auditory or motor movement (Karthik, Fathima, Rahman, Ahamed, & Ekuakille, 2013). These artifacts are not recognizable visually and called as evoked or event-related potentials (Machado et al., 2010). EEG Signals are having very low SNR and as ERPs mixed with it, are very week signal. To show the feasibility of the investigation of ERPs examination, it is important to have a high SNR estimation of EEG signals. So, the main aim of doing research in the field of EEG signal is to find out a method or technique to extract ERP waveform from the EEG recorded signal with high SNR of EEG (Paulchamy, 2017).

There were a couple of techniques proposed for Electroencephalograph signal handling and have genuine criticalness in specialized utilization (Quiroga, 2006). There is one such method by which, the noise level of EEG signals can be reduced and SNR can be enhanced is, wavelets (Wang et al., 2007). To find and remove those noisy fragments from electroencephalograph signals, discrete wavelet transform (DWT) has been associated (Asaduzzaman et al., 2010). In any case, Wavelet-based systems are very complex. As of now preliminary estimation of peak latency and amplitude of multiple correlated ERP components has additionally done (Ranjbar, Mojtaba, Mikaeili, & Banaraki, 2017). To remove the contamination from the EEG signal in a single trail, filtering can also be a good option but not using a normal filter, as the filter performance based on the statistical properties of the signal to be processed. The solution to this problem will be overcome by adaptive filters (Widrow et al., 1975). Adaptive filters are a nonlinear filter which depends upon the characteristics of the input signal. So, adaptive noise cancellation technique can be a good option for noise removal from signal (Szalai, Haller, & Marthi, 2012). In adaptive noise canceller, there is a need for an adaptive algorithm to update the weights of the filter. The literature review explores the gradient-based algorithm knows as Least Mean Square (LMS) (Górriz et al., 2009), Recursive Least Square (RLS) (Lee & Mathews, 1994) and many variants of it (Widrow, 2013; Diniz, 2008; Khanom & Islam, 2017). Recently EEG signal enhancement is also done by artificial neural network where Kalman filter is used to train it (Yakoubi, Hamdi, & Salah, 2018).

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