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

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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.

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Journal

Journal ID (iso-abbrev): J Neural Eng

Title: Journal of neural engineering

Publisher: IOP Publishing

ISSN (Electronic): 1741-2552

ISSN (Print): 1741-2552

Publication date (Electronic): May 12 2017

Volume: 14

Issue: 4

Affiliations

[1 ] Federal Institute for Occupational Safety and Health, Mental Health and Cognitive Capacity, Nöldnerstr. 40-42, 10317 Berlin, Germany.

Article

DOI: 10.1088/1741-2552/aa69d1

PubMed ID: 28497769

SO-VID: c283a60f-b736-4d2e-8297-50facb0a4624

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