Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features

Journal of Neural Engineering
T RadüntzB Meffert

Abstract

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. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. 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. 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.

References

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Citations

Jul 11, 2018·IEEE Transactions on Bio-medical Engineering·Muhammad Naveed Iqbal QureshiBoreom Lee
May 28, 2019·Frontiers in Neuroscience·Gabriella TamburroSilvia Comani
Mar 1, 2019·Sensors·Xiao JiangZean Tian
Aug 15, 2018·Neuroinformatics·Petr NejedlyPavel Jurak
Jan 30, 2021·Neural Computation·Greta TuckuteLars Kai Hansen
Oct 16, 2020·Journal of Neural Engineering·Nooshin BahadorJukka Kortelainen
Dec 2, 2021·Biomedical Physics & Engineering Express·Md Samiul Haque SunnyMohammad H Rahman

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