Characterisation and reduction of the EEG artefact caused by the helium cooling pump in the MR environment: validation in epilepsy patient data

Simultaneous and independent electroencephalography and magnetic resonance imaging: A multimodal neuroimaging dataset

We introduce an open access, multimodal neuroimaging dataset comprising simultaneously and independently collected Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data from twenty healthy, young male individuals (mean age = 26 years; SD = 3.8 years). The dataset adheres to the BIDS standard specification and is structured into two components: 1) EEG data recorded outside the Magnetic Resonance (MR) environment, inside the MR scanner without image collection and during simultaneous functional MRI acquisition (EEG-fMRI) and 2) Functional MRI data acquired with and without simultaneous EEG recording and structural MRI data obtained with and without the participants wearing the EEG cap. EEG data were recorded with an MR-compatible EEG recording system (GES 400 MR, Electrical Geodesics Inc.) using a 32-channel sponge-based EEG cap (Geodesic Sensor Net). Eyes-closed resting-state EEG data were recorded for two minutes in both the outside and inside scanner conditions and for ten minutes during simultaneous EEG-fMRI. Eyes-open resting-state EEG data were recorded for two minutes under each condition. Participants also performed an eyes opening-eyes closure block-design task outside the scanner (two minutes) and during simultaneous EEG-fMRI (four minutes). The EEG data recorded outside the scanner provides a reference signal devoid of MR-related artifacts. The data collected inside the scanner without image acquisition captures the contribution of the ballistocardiographic (BCG) without the gradient artifact, making it suitable for testing and validating BCG artifact correction methods. The EEG-fMRI data is affected by both the gradient and BCG artifacts. Brain images were acquired using a 3T GE MR750-Discovery MR scanner equipped with a 32-channel head coil. Whole-brain functional images were obtained using a GRE-EPI T2* weighted sequence (TR = 2000 ms, TE = 40 ms, 35 interleaved axial slices with 4 mm isometric voxels). Structural images were acquired using an SPGR sequence (TR = 8.1 ms, TE = 3.2 ms, flip angle = 12°, 176 sagittal slices with 1 mm isometric voxels). This stands as one of the largest open access EEG-fMRI datasets available, which allows researchers to: 1) Assess the impact of gradient and BCG artifacts on EEG data, 2) Evaluate the effectiveness of novel artifact removal techniques to minimize artifact contribution and preserve EEG signal integrity, 3) Conduct hardware/setup comparison studies, 4) Evaluate the quality of structural and functional MRI data obtained with this particular EEG system, and 5) Implement and validate multimodal integrative analysis approaches on simultaneous EEG-fMRI data.

NeuXus open-source tool for real-time artifact reduction in simultaneous EEG-fMRI

The simultaneous acquisition of electroencephalography and functional magnetic resonance imaging (EEG-fMRI) allows the complementary study of the brain's electrophysiology and hemodynamics with high temporal and spatial resolution. One application with great potential is neurofeedback training of targeted brain activity, based on the real-time analysis of the EEG and/or fMRI signals. This depends on the ability to reduce in real time the severe artifacts affecting the EEG signal acquired with fMRI, mainly the gradient and pulse artifacts. A few methods have been proposed for this purpose, but they are either slow, hardware-dependent, publicly unavailable, or proprietary software. Here, we present a fully open-source and publicly available tool for real-time EEG artifact reduction in simultaneous EEG-fMRI recordings that is fast and applicable to any hardware. Our tool is integrated in the Python toolbox NeuXus for real-time EEG processing and adapts to a real-time scenario well-established artifact average subtraction methods combined with a long short-term memory network for R peak detection. We benchmarked NeuXus on three different datasets, in terms of artifact power reduction and background signal preservation in resting state, alpha-band power reactivity to eyes closure, and event-related desynchronization during motor imagery. We showed that NeuXus performed at least as well as the only available real-time tool for conventional hardware setups (BrainVision's RecView) and a well-established offline tool (EEGLAB's FMRIB plugin). We also demonstrated NeuXus' real-time ability by reporting execution times under 250 ms. In conclusion, we present and validate the first fully open-source and hardware-independent solution for real-time artifact reduction in simultaneous EEG-fMRI studies.

Preservation of EEG spectral power features during simultaneous EEG-fMRI

Introduction

Electroencephalographic (EEG) data quality is severely compromised when recorded inside the magnetic resonance (MR) environment. Here we characterized the impact of the ballistocardiographic (BCG) artifact on resting-state EEG spectral properties and compared the effectiveness of seven common BCG correction methods to preserve EEG spectral features. We also assessed if these methods retained posterior alpha power reactivity to an eyes closure-opening (EC-EO) task and compared the results from EEG-informed fMRI analysis using different BCG correction approaches.

Method

Electroencephalographic data from 20 healthy young adults were recorded outside the MR environment and during simultaneous fMRI acquisition. The gradient artifact was effectively removed from EEG-fMRI acquisitions using Average Artifact Subtraction (AAS). The BCG artifact was corrected with seven methods: AAS, Optimal Basis Set (OBS), Independent Component Analysis (ICA), OBS followed by ICA, AAS followed by ICA, PROJIC-AAS and PROJIC-OBS. EEG signal preservation was assessed by comparing the spectral power of traditional frequency bands from the corrected rs-EEG-fMRI data with the data recorded outside the scanner. We then assessed the preservation of posterior alpha functional reactivity by computing the ratio between the EC and EO conditions during the EC-EO task. EEG-informed fMRI analysis of the EC-EO task was performed using alpha power-derived BOLD signal predictors obtained from the EEG signals corrected with different methods.

Results

The BCG artifact caused significant distortions (increased absolute power, altered relative power) across all frequency bands. Artifact residuals/signal losses were present after applying all correction methods. The EEG reactivity to the EC-EO task was better preserved with ICA-based correction approaches, particularly when using ICA feature extraction to isolate alpha power fluctuations, which allowed to accurately predict hemodynamic signal fluctuations during the EEG-informed fMRI analysis.

Discussion

Current software solutions for the BCG artifact problem offer limited efficiency to preserve the EEG spectral power properties using this particular EEG setup. The state-of-the-art approaches tested here can be further refined and should be combined with hardware implementations to better preserve EEG signal properties during simultaneous EEG-fMRI. Existing and novel BCG artifact correction methods should be validated by evaluating signal preservation of both ERPs and spontaneous EEG spectral power.

Evaluation and comparison of most prevalent artifact reduction methods for EEG acquired simultaneously with fMRI

Multimodal neuroimaging using EEG and fMRI provides deeper insights into brain function by improving the spatial and temporal resolution of the acquired data. However, simultaneous EEG-fMRI inevitably compromises the quality of the EEG and fMRI signals due to the high degree of interaction between the two systems. Fluctuations in the magnetic flux flowing through the participant and the EEG system, whether due to movement within the magnetic field of the scanner or to changes in magnetic field strength, induce electrical potentials in the EEG recordings that mask the much weaker electrical activity of the neuronal populations. A number of different methods have been proposed to reduce MR artifacts. We present an overview of the most commonly used methods and an evaluation of the methods using three sets of diverse EEG data. We limited the evaluation to open-access and easy-to-use methods and a reference signal regression method using a set of six carbon-wire loops (CWL), which allowed evaluation of their added value. The evaluation was performed by comparing EEG signals recorded outside the MRI scanner with artifact-corrected EEG signals recorded simultaneously with fMRI. To quantify and evaluate the quality of artifact reduction methods in terms of the spectral content of the signal, we analyzed changes in oscillatory activity during a resting-state and a finger tapping motor task. The quality of artifact reduction in the time domain was assessed using data collected during a visual stimulation task. In the study we utilized hierarchical Bayesian probabilistic modeling for statistical inference and observed significant differences between the evaluated methods in the success of artifact reduction and associated signal quality in both the frequency and time domains. In particular, the CWL system proved superior to the other methods evaluated in improving spectral contrast in the alpha and beta bands and in recovering visual evoked responses. Based on the results of the evaluation study, we proposed guidelines for selecting the optimal method for MR artifact reduction.

Conflict- and error-related theta activities are coupled to BOLD signals in different brain regions

Both conflict and error processing have been linked to the midfrontal theta power (4-8 Hz) increase as indicated by EEG studies and greater hemodynamic activity in the anterior midcingulate cortex (aMCC) as indicated by fMRI studies. Conveniently, the source of the midfrontal theta power was estimated in or nearby aMCC. However, previous studies using concurrent EEG and fMRI recordings in resting-state or other cognitive tasks observed only a negative relationship between theta power and BOLD signal in the brain regions typically showing task-related deactivations. In this study, we used a simultaneous EEG-fMRI technique to investigate a trial-by-trial coupling between theta power and hemodynamic activity during the performance of two conflict tasks. Independent component analysis (ICA) was applied to denoise the EEG signal and select individual midfrontal EEG components, whereas group ICA was applied to fMRI data to obtain a functional parcellation of the frontal cortex. Using a linear mixed-effect model, theta power was coupled with the peak of hemodynamic responses from various frontal, cingulate, and insular cortical sites to unravel the potential brain sources that contribute to conflict- and error-related theta variability. Although several brain regions exhibited conflict-related increases in hemodynamic activity, the conflict pre-response theta showed only a negative correlation to BOLD signal in the midline area 9 (MA9), a region exhibiting conflict-sensitive deactivation. Conversely, and more expectedly, error-related theta showed a positive relationship to activity in the aMCC. Our results provide novel evidence suggesting that the amplitude of pre-response theta reflects the process of active inhibition that suppresses the MA9 activity. This process is affected independently by the stimulus congruency, reaction times variance, and is susceptible to the time-on-task effect. Finally, it predicts the commitment of an omission error. Together, our findings highlight that conflict- and error-related theta oscillations represent fundamentally different processes.

Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?

Simultaneous EEG-fMRI has developed into a mature measurement technique in the past 25 years. During this time considerable technical and analytical advances have been made, enabling valuable scientific contributions to a range of research fields. This review will begin with an introduction to the measurement principles involved in EEG and fMRI and the advantages of combining these methods. The challenges faced when combining the two techniques will then be considered. An overview of the leading application fields where EEG-fMRI has made a significant contribution to the scientific literature and emerging applications in EEG-fMRI research trends is then presented.

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.

Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a very promising non‐invasive neuroimaging technique. However, EEG data obtained from the simultaneous EEG–fMRI are strongly influenced by MRI‐related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. When compared to the GA correction, the BCG correction is more challenging to remove due to its inherent variabilities and dynamic changes over time. The standard BCG correction (i.e., average artefact subtraction [AAS]), require detecting cardiac pulses from simultaneous electrocardiography (ECG) recording. However, ECG signals are also distorted and will become problematic for detecting reliable cardiac peaks. In this study, we focused on a beamforming spatial filtering technique to attenuate all unwanted source activities outside of the brain. Specifically, we applied the beamforming technique to attenuate the BCG artefact in EEG–fMRI, and also to recover meaningful task‐based neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond accurately. We analysed EEG–fMRI data in 20 healthy participants during the ANT, and compared four different BCG corrections (non‐BCG corrected, AAS BCG corrected, beamforming + AAS BCG corrected, beamforming BCG corrected). We demonstrated that the beamforming approach did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task‐based brain activity when compared to the standard AAS correction. This data‐driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG–fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI‐related artefacts.

Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage

Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a technique that combines temporal (largely from EEG) and spatial (largely from fMRI) indicators of brain dynamics. It is useful for understanding neuronal activity during many different event types, including spontaneous epileptic discharges, the activity of sleep stages, and activity evoked by external stimuli and decision-making tasks. However, EEG recorded during fMRI is subject to imaging, pulse, environment and motion artifact, causing noise many times greater than the neuronal signals of interest. Therefore, artifact removal methods are essential to ensure that artifacts are accurately removed, and EEG of interest is retained. This paper presents a systematic review of methods for artifact reduction in simultaneous EEG-fMRI from literature published since 1998, and an additional systematic review of EEG-fMRI studies published since 2016. The aim of the first review is to distill the literature into clear guidelines for use of simultaneous EEG-fMRI artifact reduction methods, and the aim of the second review is to determine the prevalence of artifact reduction method use in contemporary studies. We find that there are many published artifact reduction techniques available, including hardware, model based, and data-driven methods, but there are few studies published that adequately compare these methods. In contrast, recent EEG-fMRI studies show overwhelming use of just one or two artifact reduction methods based on literature published 15–20 years ago, with newer methods rarely gaining use outside the group that developed them. Surprisingly, almost 15% of EEG-fMRI studies published since 2016 fail to adequately describe the methods of artifact reduction utilized. We recommend minimum standards for reporting artifact reduction techniques in simultaneous EEG-fMRI studies and suggest that more needs to be done to make new artifact reduction techniques more accessible for the researchers and clinicians using simultaneous EEG-fMRI.

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r ≈ 0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-β frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship. This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.

On the Quality, Statistical Efficiency, and Safety of Simultaneously Recorded Multiband fMRI/EEG

The recent development of multiband functional magnetic resonance imaging (MB-fMRI) allows for the reduction of sampling period by simultaneously exciting multiple slices-the number of which is referred to as the multiband factor. Simultaneously recorded electroencephalography (EEG)/MB-fMRI has yet to be validated for data quality against conventional single band (SB)-fMRI. Pilot scans were conducted on phantoms twice and on a healthy volunteer to ensure no heating effects. In the main study, two thermometer probes were attached to 16 healthy individuals (ages 20-39, 9 females) whilst they completed two sets of 16-min resting-state and two sets of 9-min n-back task scans-each set consisting of one MB4 and one SB pulse sequence. No heating effects were reported and thermometer data showed mean increases of < 1.0 °C. Minimal differences between the two scan types were found in EEG channel variance and spectra. Expected decreases in MB4-fMRI tSNR were observed. In n-back task scans, little to no differences were detected in both EEG source analyses and fMRI local analyses for mixed effects. Resting-state posterior cingulate cortex seed-based analyses of the default mode network along with EEG-informed fMRI analysis of the occipital alpha anticorrelation effect showed improved statistical and spatial sensitivity at lower scan durations. Using EEG/MB4-fMRI for n-back tasks provided no statistical advantages nor disadvantages. However, for studying the resting-state, MB4-fMRI potentially allows for reduced scanning durations for equivalent statistical significance to be obtained or alternatively, larger effect sizes for the same scanning duration. As such, simultaneous EEG/MB4-fMRI is a viable alternative to EEG/SB-fMRI.

Artifacts in EEG of simultaneous EEG-fMRI: pulse artifact remainders in the gradient artifact template are a source of artifact residuals after average artifact subtraction

OBJECTIVE

The simultaneous application of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) opens up new ways to investigate the human brain. The EEG recordings of simultaneous EEG-fMRI, however, are overlaid to a great degree by fMRI related artifacts and an artifact reduction is mandatory before any EEG analysis. The most severe artifacts-the gradient artifact and the pulse artifact-are repetitive. Average artifact subtraction (AAS) technique exploits the repetitiveness and is presumably the most often used artifact reduction technique. In this method artifact templates are calculated by averaging over adjacent artifact epochs and subsequently the templates are subtracted to reduce the artifacts. Although the AAS technique is one of the best performing methods, artifact residuals are usually present in the resulting EEG after applying the AAS technique. This work aims at identifying sources of the artifact residuals.

APPROACH

Application of the AAS technique to artificial EEG that is contaminated with artificial fMRI related artifacts.

MAIN RESULTS

A new source of artifact residuals was identified. It was found that the AAS technique itself adds artifacts to the EEG during gradient artifact reduction, because the gradient artifact template is corrupted by pulse artifact remainders.

SIGNIFICANCE

This work shows that using a standard number of 25 epochs to calculate the gradient artifact template-as suggested by the inventors of AAS-results in substantial artifact residuals and consequently to a low EEG quality. Furthermore, the work discusses how potential solutions to this problem have serious side effects such as loss of adaptivity of the AAS technique. Hence, this problem must be considered carefully already in the design of simultaneous EEG-fMRI experiments.

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

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.

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

OBJECTIVE

Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity.

APPROACH

The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component's time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns.

MAIN RESULTS

We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network.

SIGNIFICANCE

Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

EEG-Informed fMRI: A Review of Data Analysis Methods

The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.

EEG synchronization measures predict epilepsy-related BOLD-fMRI fluctuations better than commonly used univariate metrics

OBJECTIVE

We hypothesize that the hypersynchronization associated with epileptic activity is best described by EEG synchronization measures, and propose to use these as predictors of epilepsy-related BOLD fluctuations.

METHODS

We computed the phase synchronization index (PSI) and global field synchronization (GFS), within two frequency bands, a broadband (1-45 Hz) and a narrower band focused on the presence of epileptic activity (3-10 Hz). The associated epileptic networks were compared with those obtained using conventional unitary regressors and two power-weighted metrics (total power and root mean square frequency), on nine simultaneous EEG-fMRI datasets from four epilepsy patients, exhibiting inter-ictal epileptiform discharges (IEDs).

RESULTS

The average PSI within 3-10 Hz achieved the best performance across several measures reflecting reliability in all datasets. The results were cross-validated through electrical source imaging of the IEDs. The applicability of PSI when no IEDs are recorded on the EEG was evaluated on three additional patients, yielding partially plausible networks in all cases.

CONCLUSIONS

Epileptic networks can be mapped based on the EEG PSI metric within an IED-specific frequency band, performing better than commonly used EEG metrics.

SIGNIFICANCE

This is the first study to investigate EEG synchronization measures as potential predictors of epilepsy-related BOLD fluctuations.

Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF)

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI

OBJECTIVE

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners.

APPROACH

To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels.

MAIN RESULTS

The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (<35 Hz) frequency ranges. MBRLAF is computationally more demanding than RLAF, but highly effective in all EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best.

SIGNIFICANCE

In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We introduce new algorithms for reducing EEG artifacts due to simultaneous fMRI The algorithms combine a reference layer and adaptive filtering Several evaluation criteria suggest superior effectivity in terms of artifact reduction We demonstrate that physiological EEG components are preserved.

Ballistocardiogram artifact correction taking into account physiological signal preservation in simultaneous EEG-fMRI

The ballistocardiogram (BCG) artifact is currently one of the most challenging in the EEG acquired concurrently with fMRI, with correction invariably yielding residual artifacts and/or deterioration of the physiological signals of interest. In this paper, we propose a family of methods whereby the EEG is decomposed using Independent Component Analysis (ICA) and a novel approach for the selection of BCG-related independent components (ICs) is used (PROJection onto Independent Components, PROJIC). Three ICA-based strategies for BCG artifact correction are then explored: 1) BCG-related ICs are removed from the back-reconstruction of the EEG (PROJIC); and 2-3) BCG-related ICs are corrected for the artifact occurrences using an Optimal Basis Set (OBS) or Average Artifact Subtraction (AAS) framework, before back-projecting all ICs onto EEG space (PROJIC-OBS and PROJIC-AAS, respectively). A novel evaluation pipeline is also proposed to assess the methods performance, which takes into account not only artifact but also physiological signal removal, allowing for a flexible weighting of the importance given to physiological signal preservation. This evaluation is used for the group-level parameter optimization of each algorithm on simultaneous EEG-fMRI data acquired using two different setups at 3T and 7T. Comparison with state-of-the-art BCG correction methods showed that PROJIC-OBS and PROJIC-AAS outperformed the others when priority was given to artifact removal or physiological signal preservation, respectively, while both PROJIC-AAS and AAS were in general the best choices for intermediate trade-offs. The impact of the BCG correction on the quality of event-related potentials (ERPs) of interest was assessed in terms of the relative reduction of the standard error (SE) across trials: 26/66%, 32/62% and 18/61% were achieved by, respectively, PROJIC, PROJIC-OBS and PROJIC-AAS, for data collected at 3T/7T. Although more significant improvements were achieved at 7T, the results were qualitatively comparable for both setups, which indicate the wide applicability of the proposed methodologies and recommendations.

"Eyes Open - Eyes Closed" EEG/fMRI data set including dedicated "Carbon Wire Loop" motion detection channels

This data set contains electroencephalography (EEG) data as well as simultaneous EEG with functional magnetic resonance imaging (EEG/fMRI) data. During EEG/fMRI, the EEG cap was outfitted with a hardware-based add-on consisting of carbon-wire loops (CWL). These yielded six extra׳CWL׳ signals related to Faraday induction of these loops in the main magnetic field “Measurement and reduction of motion and ballistocardiogram artefacts from simultaneous EEG and fMRI recordings” (Masterton et al., 2007) [1].

In this data set, the CWL data make it possible to do a direct regression approach to deal with the BCG and specifically He artifact.

The CWL-EEG/fMRI data in this paper has been recorded on two MRI scanners with different Helium pump systems (4 subjects on a 3 T TIM Trio and 4 subjects on a 3T VERIO). Separate EEG/fMRI data sets have been recorded for the helium pump ON as well as the helium pump OFF conditions. The EEG-only data (same subjects) has been recorded for a motion artifact-free reference EEG signal outside of the scanner.

This paper also links to an EEGlab “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis” (Delorme and Makeig, 2004) [2] plugin to perform a CWL regression approach to deal with the He pump artifact, as published in the main paper “Carbon-wire loop based artifact correction outperforms post-processing EEG/fMRI corrections-A validation of a real-time simultaneous EEG/fMRI correction method” (van der Meer et al., 2016) [3].

Objective selection of epilepsy-related independent components from EEG data

BACKGROUND

Independent Component Analysis (ICA) is commonly used for the identification of sources of interest in electroencephalographic (EEG) data, but the selection of the relevant components remains an open issue depending on the specific application.

NEW METHOD

We propose a novel approach for the objective selection of epilepsy-related independent components (ICs) from EEG data collected during functional Magnetic Resonance Imaging (fMRI) acquisitions, called PROJection onto Independent Components (PROJIC). Inter-ictal epileptiform discharges (IEDs) are identified on a reference EEG dataset collected outside the MRI scanner by an expert neurophysiologist, and the resulting average IED is projected onto the IC space of the EEG data collected simultaneously with fMRI. The power of the IED projection is then used to inform a k-means clustering algorithm of the ICs, allowing for the classification of epilepsy-related ICs.

COMPARISON WITH EXISTING METHODS

The performance of PROJIC was compared with two methods previously proposed for the objective selection of EEG ICs of interest, which are based on the explicit similarity of the ICs with spatio-temporal templates of the events of interest, instead of the projection power.

RESULTS

The proposed PROJIC method outperformed the others for both artificial and real data (19 datasets collected from 6 patients with drug-refractory focal epilepsy), with an average accuracy of 98.6%.

CONCLUSIONS

The ability of our method to accurately and objectively select epilepsy-related ICs makes it an important contribution for simultaneous EEG-fMRI epilepsy studies, with potential applications in the analysis of event-related EEG activity more generally, and also in EEG artefact correction.