Removal of Eye-Blink Artifact from EEG Using LDA and Pre-trained RBF Neural Network

Abstract

Electroencephalography (EEG) data are highly susceptible to noise and are frequently corrupted with eye-blink artifacts. Methods based on independent component analysis (ICA) and discrete wavelet transform (DWT) have been used as a standard for removal of such kinds of artifacts. However, these methods often require visual inspection and appropriate thresholding for identifying and removing artifactual components from the EEG signal. The proposed method presents a windowed method, where an LDA classifier is used for identification of artifacts and RBF neural network is used for correcting artifacts. In the present work, we propose a robust and automated method for identification and removal of artifacts from EEG signals, without the need for any visual inspection or threshold selection. Using test data contaminated with eye-blink artifacts, it is observed that our proposed method performs better in identifying and removing artifactual components from EEG data than the existing thresholding methods and does not require the application of ICA for identification of artifacts and can also be applied to any number of channels.