Literature DB >> 25570299

A method for quantitative assessment of artifacts in EEG, and an empirical study of artifacts.

Simon L Kappel, David Looney, Danilo P Mandic, Preben Kidmose.   

Abstract

Wearable EEG systems for continuous brain monitoring is an emergent technology that involves significant technical challenges. Some of these are related to the fact that these systems operate in conditions that are far less controllable with respect to interference and artifacts than is the case for conventional systems. Quantitative assessment of artifacts provides a mean for optimization with respect to electrode technology, electrode location, electronic instrumentation and system design. To this end, we propose an artifact assessment method and evaluate it over an empirical study of 3 subjects and 5 different types of artifacts. The study showed consistent results across subjects and artifacts.

Mesh:

Year:  2014        PMID: 25570299     DOI: 10.1109/EMBC.2014.6943931

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  3 in total

1.  A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG.

Authors:  Pasin Israsena; Setha Pan-Ngum
Journal:  Front Comput Neurosci       Date:  2022-05-19       Impact factor: 3.387

2.  Physiological artifacts in scalp EEG and ear-EEG.

Authors:  Simon L Kappel; David Looney; Danilo P Mandic; Preben Kidmose
Journal:  Biomed Eng Online       Date:  2017-08-11       Impact factor: 2.819

3.  EEG Recorded from the Ear: Characterizing the Ear-EEG Method.

Authors:  Kaare B Mikkelsen; Simon L Kappel; Danilo P Mandic; Preben Kidmose
Journal:  Front Neurosci       Date:  2015-11-18       Impact factor: 4.677

  3 in total

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