Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features. (12th May 2017)

• Record Type: Journal Article
• Authors: Radüntz, Thea; Scouten, Jon; Hochmuth, Olaf; Meffert, Beate
• Is Part Of: Journal of neural engineering. Volume 14:Number 4(2017:Aug.)
• Publication Date: 2017-05-12
• Subjects: electroencephalography (EEG) -- biomedical signal processing -- artifact elimination -- independent component analysis -- image filtering -- support vector machines (SVM) -- artificial neural networks (ANN); Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/aa69d1
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. Approach . In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. Main results . We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Significance . Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.