Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Haumann, Niels Trusbak Parkkonen, Lauri Kliuchko, Marina Vuust, Peter Brattico, Elvira 2017-05-11T07:38:36Z 2017-05-11T07:38:36Z 2016
dc.identifier.citation Haumann , N T , Parkkonen , L , Kliuchko , M , Vuust , P & Brattico , E 2016 , ' Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study ' COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE , vol 2016 , 7489108 , pp. 1-10 . DOI: 10.1155/2016/7489108 en
dc.identifier.issn 1687-5265
dc.identifier.issn 1687-5273
dc.identifier.other PURE UUID: 66427c8c-9685-4c75-b88a-ab011c0c1899
dc.identifier.other PURE ITEMURL:
dc.identifier.other PURE LINK:
dc.identifier.other PURE FILEURL:
dc.description.abstract We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal - slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the waveform when the signal-to-noise ratio (SNR) in the original data is relatively low - in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR. en
dc.format.extent 1-10
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries Volume 2016 en
dc.rights openAccess en
dc.subject.other Computer Science(all) en
dc.subject.other Mathematics(all) en
dc.subject.other Neuroscience(all) en
dc.subject.other 113 Computer and information sciences en
dc.title Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Aarhus University
dc.contributor.department Department of Neuroscience and Biomedical Engineering
dc.contributor.department University of Helsinki
dc.subject.keyword Computer Science(all)
dc.subject.keyword Mathematics(all)
dc.subject.keyword Neuroscience(all)
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201705113950
dc.identifier.doi 10.1155/2016/7489108
dc.type.version publishedVersion

Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search archive

Advanced Search

article-iconSubmit a publication


My Account