Automated artifact rejection using ICA and image processing algorithms

Abstract

This paper discuses about the automatic Electroencephalogram (EEG) artifact removal using Independent Component Analysis (ICA) and Image Processing Algorithms. ICA is used for obtaining the Independent Components (IC's) that are linear in nature from the EEG signal. These IC's also known as topoplots are used for feature extraction and classification. Local binary pattern (LBP) is being utilized to obtain the features. These features are used for training the classifiers and helps in achieving automatic artifact elimination. Classifiers such as Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) are used for identifying the artifacts and thereby to remove such signals. The remaining IC components are used for reconstructing the de-noised signal. Of the existing methods mentioned, with respect to artifact elimination methods, LDA gives the best performance in artifact removal and helps in reconstruction of the signal.