Comparative analysis of ICA, PCA-based EASI and wavelet-based unsupervised denoising for EEG signals

Abstract

Electroencephalography (EEG) can be used to study various brain activities related to human responses and disorders. EEG signal is prone to noises which are caused due to eye movements, power-line interference, muscle movements, etc. Therefore, to obtain refinedEEGsignals for further processing, it should be denoised. There are several methods by which EEG signals can be denoised, among which we have used Independent Component Analysis (ICA), Principal Component Analysis (PCA)-based Equivariant Adaptive Separation by Independence (EASI), and Wavelet-based unsupervised denoising methods. The performance of these methods is compared using Signal-to-Noise Ratio (SNR) and Percentage Root-mean-square Difference (PRD).