Suppression of overlearning in independent component analysis used for removal of muscular artifacts from electroencephalographic records

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00322574" target="_blank" >RIV/68407700:21230/18:00322574 - isvavai.cz</a>

Result on the web

<a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201900" target="_blank" >http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201900</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pone.0201900" target="_blank" >10.1371/journal.pone.0201900</a>

Result language

angličtina

Original language name

Suppression of overlearning in independent component analysis used for removal of muscular artifacts from electroencephalographic records

Original language description

This paper addresses the overlearning problem in the independent component analysis (ICA) used for the removal of muscular artifacts from electroencephalographic (EEG) records. We note that for short EEG records with high number of channels the ICA fails to separate artifact-free EEG and muscular artifacts, which has been previously attributed to the phenomenon called overlearning. We address this problem by projecting an EEG record into several subspaces with a lower dimension, and perform the ICA on each subspace separately. Due to a reduced dimension of the subspaces, the overlearning is suppressed, and muscular artifacts are better separated. Once the muscular artifacts are removed, the signals in the individual subspaces are combined to provide an artifact free EEG record. We show that for short signals and high number of EEG channels our approach outperforms the currently available ICA based algorithms for muscular artifact removal. The proposed technique can efficiently suppress ICA overlearning for short signal segments of high density EEG signals.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

PLoS ONE

ISSN

1932-6203

e-ISSN

1932-6203

Volume of the periodical

13

Issue of the periodical within the volume

8

Country of publishing house

US - UNITED STATES

Number of pages

21

Pages from-to

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UT code for WoS article

000441662800016

EID of the result in the Scopus database

2-s2.0-85051552389