Abstract
Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow for a non-invasive investigation of cerebral functions with high temporal and spatial resolution. The main challenge of such integration is the removal of the pulse artefact (PA) that affects EEG signals recorded in the magnetic resonance (MR) scanner. Often applied techniques for this purpose are Optimal Basis Set (OBS) and Independent Component Analysis (ICA). The combination of OBS and ICA is increasingly used, since it can potentially improve the correction performed by each technique separately. The present study is focused on the OBS-ICA combination and is aimed at providing the optimal ICA parameters for PA correction in resting-state EEG data, where the information of interest is not specified in latency and amplitude as in, for example, evoked potential. A comparison between two intervals for ICA calculation and four methods for marking artefactual components was performed. The performance of the methods was discussed in terms of their capability to 1) remove the artefact and 2) preserve the information of interest. The analysis included 12 subjects and two resting-state datasets for each of them. The results showed that none of the signal lengths for the ICA calculation was highly preferable to the other. Among the methods for the identification of PA-related components, the one based on the wavelets transform of each component emerged as the best compromise between the effectiveness in removing PA and the conservation of the physiological neuronal content.
| Original language | English |
|---|---|
| Article number | e112147 |
| Journal | PLoS One |
| Volume | 9 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - Nov 10 2014 |
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ASJC Scopus subject areas
- Agricultural and Biological Sciences(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Medicine(all)
Cite this
Removal of pulse artefact from EEG data recorded in MR environment at 3T. setting of ICA parameters for marking artefactual components : Application to resting-state data. / Maggioni, Eleonora; Arrubla, Jorge; Warbrick, Tracy; Dammers, Jürgen; Bianchi, Anna M.; Reni, Gianluigi; Tosetti, Michela; Neuner, Irene; Shah, N. Jon.
In: PLoS One, Vol. 9, No. 11, e112147, 10.11.2014.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Removal of pulse artefact from EEG data recorded in MR environment at 3T. setting of ICA parameters for marking artefactual components
T2 - Application to resting-state data
AU - Maggioni, Eleonora
AU - Arrubla, Jorge
AU - Warbrick, Tracy
AU - Dammers, Jürgen
AU - Bianchi, Anna M.
AU - Reni, Gianluigi
AU - Tosetti, Michela
AU - Neuner, Irene
AU - Shah, N. Jon
PY - 2014/11/10
Y1 - 2014/11/10
N2 - Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow for a non-invasive investigation of cerebral functions with high temporal and spatial resolution. The main challenge of such integration is the removal of the pulse artefact (PA) that affects EEG signals recorded in the magnetic resonance (MR) scanner. Often applied techniques for this purpose are Optimal Basis Set (OBS) and Independent Component Analysis (ICA). The combination of OBS and ICA is increasingly used, since it can potentially improve the correction performed by each technique separately. The present study is focused on the OBS-ICA combination and is aimed at providing the optimal ICA parameters for PA correction in resting-state EEG data, where the information of interest is not specified in latency and amplitude as in, for example, evoked potential. A comparison between two intervals for ICA calculation and four methods for marking artefactual components was performed. The performance of the methods was discussed in terms of their capability to 1) remove the artefact and 2) preserve the information of interest. The analysis included 12 subjects and two resting-state datasets for each of them. The results showed that none of the signal lengths for the ICA calculation was highly preferable to the other. Among the methods for the identification of PA-related components, the one based on the wavelets transform of each component emerged as the best compromise between the effectiveness in removing PA and the conservation of the physiological neuronal content.
AB - Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow for a non-invasive investigation of cerebral functions with high temporal and spatial resolution. The main challenge of such integration is the removal of the pulse artefact (PA) that affects EEG signals recorded in the magnetic resonance (MR) scanner. Often applied techniques for this purpose are Optimal Basis Set (OBS) and Independent Component Analysis (ICA). The combination of OBS and ICA is increasingly used, since it can potentially improve the correction performed by each technique separately. The present study is focused on the OBS-ICA combination and is aimed at providing the optimal ICA parameters for PA correction in resting-state EEG data, where the information of interest is not specified in latency and amplitude as in, for example, evoked potential. A comparison between two intervals for ICA calculation and four methods for marking artefactual components was performed. The performance of the methods was discussed in terms of their capability to 1) remove the artefact and 2) preserve the information of interest. The analysis included 12 subjects and two resting-state datasets for each of them. The results showed that none of the signal lengths for the ICA calculation was highly preferable to the other. Among the methods for the identification of PA-related components, the one based on the wavelets transform of each component emerged as the best compromise between the effectiveness in removing PA and the conservation of the physiological neuronal content.
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UR - http://www.scopus.com/inward/citedby.url?scp=84911391093&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0112147
DO - 10.1371/journal.pone.0112147
M3 - Article
C2 - 25383625
AN - SCOPUS:84911391093
VL - 9
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 11
M1 - e112147
ER -