Abstract
Combination of electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) plays a potential role in neuroimaging due to its high spatial and temporal resolution. However, EEG is easily influenced by ballistocardiogram (BCG) artifacts and may cause false identification of the related EEG features, such as epileptic spikes. There are many related methods to remove them, however, they do not consider the time-varying features of BCG artifacts. In this paper, a novel method using clustering algorithm to catch the BCG artifacts' features and together with the constrained ICA (ccICA) is proposed to remove the BCG artifacts. We first applied this method to the simulated data, which was constructed by adding the BCG artifacts to the EEG signal obtained from the conventional environment. Then, our method was tested to demonstrate the effectiveness during EEG and fMRI experiments on 10 healthy subjects. In simulated data analysis, the value of error in signal amplitude (Er) computed by ccICA method was lower than those from other methods including AAS, OBS, and cICA (p < 0.005). In vivo data analysis, the Improvement of Normalized Power Spectrum (INPS) calculated by ccICA method in all electrodes was much higher than AAS, OBS, and cICA methods (p < 0.005). We also used other evaluation index (e.g., power analysis) to compare our method with other traditional methods. In conclusion, our novel method successfully and effectively removed BCG artifacts in both simulated and vivo EEG data tests, showing the potentials of removing artifacts in EEG-fMRI applications.
Highlights
Simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging can make full use of the complementarity between the high temporal resolution of EEG and the millimeter spatial resolution of fMRI when studying brain activity (Jorge et al, 2014; Murta et al, 2015)
We propose a novel method of clusteringconstraint ICA to remove the BCG artifacts which takes into account artifacts’ time-varied shape, amplitude, and scale
Varying BCG artifacts were added to the original EEG data to generate the simulated data
Summary
Simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) can make full use of the complementarity between the high temporal resolution of EEG and the millimeter spatial resolution of fMRI when studying brain activity (Jorge et al, 2014; Murta et al, 2015). Simultaneous EEG-fMRI acquisition is an important tool for further understanding of brain function and dysfunction including neurofeedback (Zotev et al, 2014), recognition memory, epilepsy (Dong et al, 2015, 2016), and schizophrenia (Ford et al, 2016) etc This neuroimaging technique has one disadvantage that two major artifacts, gradient artifacts (GA) and ballistocardiogram (BCG) artifacts, can be induced on EEG data. The amplitude of the BCG artifacts is proportional to the intensity of the magnetic field inside the MRI scanner (Yan et al, 2010; Mullinger et al, 2013) and its shape changes over time (Debener et al, 2008) These characteristics of the BCG artifacts make it hard to predict and characterize, causing troubles in artifact removal. In EEG-fMRI applications, BCG artifact removal is a meaningful issue and more difficult than GA removal (Laufs et al, 2008)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
