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.
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.
Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Jülich, Germany; Department of Electronics, Information and Bioengineering, DEIB, Politecnico di Milano, Milano, Italy; Bioengineering Lab, Scientific Institute IRCCS E. Medea, Bosisio Parini, Italy.
Eleonora
E
Maggioni
Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany.
Jorge
J
Arrubla
Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Jülich, Germany.
Tracy
T
Warbrick
Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Jülich, Germany.
Jürgen
J
Dammers
Department of Electronics, Information and Bioengineering, DEIB, Politecnico di Milano, Milano, Italy.
Anna M
AM
Bianchi
Bioengineering Lab, Scientific Institute IRCCS E. Medea, Bosisio Parini, Italy.
Gianluigi
G
Reni
Laboratory of Medical Physics and Magnetic Resonance Technologies, IRCCS Fondazione Stella Maris, Pisa, Italy.
Michela
M
Tosetti
Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany; JARA - BRAIN - Translational Medicine, RWTH Aachen University, Aachen, Germany.
Irene
I
Neuner
Institute of Neuroscience and Medicine 4, INM 4, Forschungszentrum Jülich, Jülich, Germany; JARA - BRAIN - Translational Medicine, RWTH Aachen University, Aachen, Germany; Department of Neurology, Faculty of Medicine, JARA, RWTH Aachen University, Aachen, Germany.
N Jon
NJ
Shah
D000328:Adult; D016477:Artifacts; D001921:Brain / Q000502:physiology; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D015203:Reproducibility of Results; D012146:Rest; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors