Single Shot Reversible GAN for BCG artifact removal in simultaneous EEG-fMRI

Simultaneous EEG-fMRI acquisition and analysis technology has been widely used in various research fields of brain science. However, how to remove the ballistocardiogram (BCG) artifacts in this scenario remains a huge challenge. Because it is impossible to obtain clean and BCG-contaminated EEG signals at the same time, BCG artifact removal is a typical unpaired signal-to-signal problem. To solve this problem, this paper proposed a new GAN training model - Single Shot Reversible GAN (SSRGAN). The model is allowing bidirectional input to better combine the characteristics of the two types of signals, instead of using two independent models for bidirectional conversion as in the past. Furthermore, the model is decomposed into multiple independent convolutional blocks with specific functions. Through additional training of the blocks, the local representation ability of the model is improved, thereby improving the overall model performance. Experimental results show that, compared with existing methods, the method proposed in this paper can remove BCG artifacts more effectively and retain the useful EEG information.Comment: 8 pages, 5 figures, 1 tabl

Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression

Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI.National Institutes of Health (U.S.) (Award DP1-OD003646)National Institutes of Health (U.S.) (Award TR01-GM104948)National Institutes of Health (U.S.) (Grant R44NS071988)National Institute of Neurological Diseases and Stroke (U.S.) (Grant Grant R44NS071988

When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI.

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision

The neural bases of event monitoring across domains: a simultaneous ERP-fMRI study.

The ability to check and evaluate the environment over time with the aim to detect the occurrence of target stimuli is supported by sustained/tonic as well as transient/phasic control processes, which overall might be referred to as event monitoring. The neural underpinning of sustained control processes involves a fronto-parietal network. However, it has not been well-defined yet whether this cortical circuit acts irrespective of the specific material to be monitored and whether this mediates sustained as well as transient monitoring processes. In the current study, the functional activity of brain during an event monitoring task was investigated and compared between two cognitive domains, whose processing is mediated by differently lateralized areas. Namely, participants were asked to monitor sequences of either faces (supported by right-hemisphere regions) or tools (left-hemisphere). In order to disentangle sustained from transient components of monitoring, a simultaneous EEG-fMRI technique was adopted within a block design. When contrasting monitoring versus control blocks, the conventional fMRI analysis revealed the sustained involvement of bilateral fronto-parietal regions, in both task domains. Event-related potentials (ERPs) showed a more positive amplitude over frontal sites in monitoring compared to control blocks, providing evidence of a transient monitoring component. The joint ERP-fMRI analysis showed that, in the case of face monitoring, these transient processes rely on right-lateralized areas, including the inferior parietal lobule and the middle frontal gyrus. In the case of tools, no fronto-parietal areas correlated with the transient ERP activity, suggesting that in this domain phasic monitoring processes were masked by tonic ones. Overall, the present findings highlight the role of bilateral fronto-parietal regions in sustained monitoring, independently of the specific task requirements, and suggest that right-lateralized areas subtend transient monitoring processes, at least in some task contexts