Denoising of EEG Signal using Matlab and SIMULINK Techniques and Estimation of Power Spectral Density of EEG Signal using SIMULINK AR Models

B.Krishna Kumar

doi:10.35940/ijeat.f8956.129219

Abstract

The Electroencephalogram (EEG) is the standard technique for investigating the brain’s electrical activity in different psychological and pathological states. Analysis of Electroencephalogram (EEG) signal is a challenging task by reason of the presence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. Normally EEG signals falls in the frequency range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex. In this research paper, removal of artifacts was done using wavelets (matlab coding) as well as using SIMULINK DWT and IDWT blocks and estimated the SNR. In the next stage the output of IDWT block was taken as input to Burg model and Yule walker model to estimate the power spectral density of EEG signal by setting the various parameters of the blocks. The implementation of denoising of EEG signal using SIMULINK DWT and IDWT blocks and estimation of power spectral density of denoised EEG signal using Burg model and Yule walker model was explained in detail in the paper under the methodology heading. In this research paper, the collected EEG signal is normalized and later linearly mixed with the normalized EOG signal resulting in a noisy EEG signal. This noisy EEG signal is decomposed to 4 levels by using different wavelets. This decomposition of EEG signals yields approximate and detail coefficients. Later different thresholding techniques were applied to detail coefficients and estimated the Signal to Noise Ratio of it and estimated the power spectral density of denoised EEG signal obtained from dB4 wavelet as it is providing better SNR than other wavelets mentioned in the results.

Highlights

Electroencephalogram (EEG) is a standard non-invasive technique with which one can record the brain’s electrical activity over a period of time, using electrodes
In the stage the output of IDWT block was taken as input to Burg model and Yule walker model to estimate the power spectral density of EEG signal by setting the various parameters of the blocks
Later different thresholding techniques were applied to detail coefficients and estimated the Signal to Noise Ratio of it and estimated the power spectral density of denoised EEG signal obtained from dB4 wavelet as it is providing better SNR than other wavelets mentioned in the results

Summary

INTRODUCTION

Electroencephalogram (EEG) is a standard non-invasive technique with which one can record the brain’s electrical activity over a period of time, using electrodes. This is very much useful in diagnosing the disorders of human brain such as epilepsy, seizures and brain damage. The EEG signals can be contaminated by various artifacts, of which the major noise source is ocular artifact. Methods were implemented in removing eye blinks artifacts These methods require a reliable reference channel. Noisy EEG signal was decomposed to four levels using different wavelets This decomposition gives low frequency and high frequency components of noisy. The above mentioned procedure has been carried out both in matlab and SIMULINK

METHODOLOGY

Method

CONCLUSION

RESULTS