Detail
Article
Online article
FT
Medvik - BMC
  • Something wrong with this record ?

Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI

M. Marino, Q. Liu, V. Koudelka, C. Porcaro, J. Hlinka, N. Wenderoth, D. Mantini,

. 2018 ; 8 (1) : 8902. [pub] 20180611

Language English Country Great Britain

Document type Journal Article, Research Support, Non-U.S. Gov't

Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.

References provided by Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc19045416
003      
CZ-PrNML
005      
20200113082156.0
007      
ta
008      
200109s2018 xxk f 000 0|eng||
009      
AR
024    7_
$a 10.1038/s41598-018-27187-6 $2 doi
035    __
$a (PubMed)29891929
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxk
100    1_
$a Marino, Marco $u Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium. Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Neural Control of Movement Laboratory, ETH Zurich, Zurich, Switzerland.
245    10
$a Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI / $c M. Marino, Q. Liu, V. Koudelka, C. Porcaro, J. Hlinka, N. Wenderoth, D. Mantini,
520    9_
$a Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.
650    _2
$a dospělí $7 D000328
650    12
$a artefakty $7 D016477
650    _2
$a elektroencefalografie $x metody $7 D004569
650    _2
$a ženské pohlaví $7 D005260
650    _2
$a zdraví dobrovolníci pro lékařské studie $7 D064368
650    _2
$a lidé $7 D006801
650    _2
$a počítačové zpracování obrazu $x metody $7 D007091
650    _2
$a magnetická rezonanční tomografie $x metody $7 D008279
650    _2
$a mužské pohlaví $7 D008297
650    _2
$a multimodální zobrazování $x metody $7 D064847
650    _2
$a mladý dospělý $7 D055815
655    _2
$a časopisecké články $7 D016428
655    _2
$a práce podpořená grantem $7 D013485
700    1_
$a Liu, Quanying $u Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium. Neural Control of Movement Laboratory, ETH Zurich, Zurich, Switzerland.
700    1_
$a Koudelka, Vlastimil $u National Institute of Mental Health, Klecany, Czech Republic.
700    1_
$a Porcaro, Camillo $u Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium. LET'S-ISTC, National Research Council, Rome, Italy. Birmingham University Imaging Centre (BUIC), School of Psychology, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
700    1_
$a Hlinka, Jaroslav $u National Institute of Mental Health, Klecany, Czech Republic. Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic.
700    1_
$a Wenderoth, Nicole $u Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium. Neural Control of Movement Laboratory, ETH Zurich, Zurich, Switzerland.
700    1_
$a Mantini, Dante $u Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium. dante.mantini@kuleuven.be. Functional Neuroimaging Laboratory, IRCCS San Camillo Hospital Foundation, Venice Lido, Italy. dante.mantini@kuleuven.be.
773    0_
$w MED00182195 $t Scientific reports $x 2045-2322 $g Roč. 8, č. 1 (2018), s. 8902
856    41
$u https://pubmed.ncbi.nlm.nih.gov/29891929 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y a $z 0
990    __
$a 20200109 $b ABA008
991    __
$a 20200113082527 $b ABA008
999    __
$a ok $b bmc $g 1483685 $s 1084089
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2018 $b 8 $c 1 $d 8902 $e 20180611 $i 2045-2322 $m Scientific reports $n Sci Rep $x MED00182195
LZP    __
$a Pubmed-20200109

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...