Pulse Artifact Research Articles

Case Report: Case report: Aslanger’s sign in electrocardiogram.

Electrocardiograms (ECGs) can be affected by various factors and technical problems. It is rare for an artefact to be the cause of ST-segment elevation, especially in asymptomatic patients. An important distinction between true ST segment elevation caused by myocardial infarction and an artefact is that the baseline elevation in an artefact may begin before or after the appearance of the QRS complex. When confronted with an abnormal ECG with suspicious waveform contours and possibly only one completely normal limb leads, the diagnosis of arterial pulse artefact should be considered. It is important to exclude subjective assessments unless they are clearly labelled as such.

Case Report: Case report: Aslanger’s sign in electrocardiogram.

Electrocardiograms (ECGs) can be affected by various factors and technical problems. It is rare for an artefact to be the cause of ST-segment elevation, especially in asymptomatic patients. An important distinction between true ST segment elevation caused by myocardial infarction and an artefact is that the baseline elevation in an artefact may begin before or after the appearance of the QRS complex. When confronted with an abnormal ECG with suspicious waveform contours and possibly only one completely normal limb lead, the diagnosis of arterial pulse artefact should be considered. It is important to exclude subjective assessments unless they are clearly labelled as such. When encountering an abnormal ECG with suspicious waveform contours and possibly only one completely normal limb lead, the diagnosis of arterial pulse artefact should be considered.

Transcranial magnetic stimulation of primary motor cortex elicits an immediate transcranial evoked potential

BackgroundTranscranial evoked potentials (TEPs) measured via electroencephalography (EEG) are widely used to study the cortical responses to transcranial magnetic stimulation (TMS). Immediate transcranial evoked potentials (i-TEPs) have been obscured by pulse and muscular artifacts. Thus, the TEP peaks that are commonly reported have latencies that are too long to be caused by direct excitation of cortical neurons. MethodsIn 25 healthy individuals, we recorded i-TEPs evoked by a single biphasic TMS pulse targeting the primary motor hand area (M1HAND) or parietal or midline control sites. Sampling EEG at 50 kHz enabled us to reduce the duration of the TMS pulse artifact to a few milliseconds, while minor adjustments of the TMS coil tilt or position enabled us to avoid cranial muscular twitches during the experiment. ResultsWe observed an early positive EEG deflection starting after approx. 2 ms followed by a series of superimposed peaks with an inter-peak interval of ∼1.1–1.4 ms in multiple electrodes surrounding the stimulated sensorimotor region. This multi-peak i-TEP response was only evoked by TMS of the M1HAND region and was modified by changes in stimulation intensity and current direction. DiscussionSingle-pulse TMS of the M1HAND evokes an immediate local multi-peak response at the cortical site of stimulation. Our results suggest that the observed i-TEP patterns are genuine cortical responses evoked by TMS caused by synchronized excitation of pyramidal neurons in the targeted precentral cortex. This notion needs to be corroborated in future studies, including further investigations into the potential contribution of instrumental or physiological artifacts.

The Effect of Stimulation Intensity, Sampling Frequency, and Sample Synchronization in TMS-EEG on the TMS Pulse Artifact Amplitude and Duration.

Transcranial magnetic stimulation (TMS) coupled with electroencephalography (EEG) possesses diagnostic and therapeutic benefits. However, TMS provokes a large pulse artifact that momentarily obscures the cortical response, presenting a significant challenge for EEG data interpretation. We examined how stimulation intensity (SI), EEG sampling frequency (Fs) and synchronization of stimulation with EEG sampling influence the amplitude and duration of the pulse artifact. In eight healthy subjects, single-pulse TMS was administered to the primary motor cortex, due to its well-documented responsiveness to TMS. We applied two different SIs (90% and 120% of resting motor threshold, representing the commonly used subthreshold and suprathreshold levels) and Fs (conventional 5 kHz and high frequency 20 kHz) both with TMS synchronized with the EEG sampling and the conventional non-synchronized setting. Aside from removal of the DC-offset and epoching, no preprocessing was performed to the data. Using a random forest regression model, we identified that Fs had the largest impact on both the amplitude and duration of the pulse artifact, with median variable importance values of 1.444 and 1.327, respectively, followed by SI (0.964 and 1.083) and sampling synchronization (0.223 and 0.248). This indicated that Fs and SI are crucial for minimizing prediction error and thus play a pivotal role in accurately characterizing the pulse artifact. The results of this study enable focusing some of the study design parameters to minimize TMS pulse artifact, which is essential for both enhancing the reliability of clinical TMS-EEG applications and improving the overall integrity and interpretability of TMS-EEG data.

A three-dimensional adaptive rational interpolation algorithm for removing TMS-EEG pulse artifacts

Objective. Transcranial magnetic stimulation in combination with electroencephalography (TMS-EEG) has been widely used to study the reactivity and connectivity of brain regions. In order to efficiently and fast solve the pulse artifacts problem caused by TMS electromagnetic pulses, a three-dimensional adaptive rational quadratic Hermite interpolation algorithm is proposed. Approach. Firstly, a three-dimensional signal matrix is obtained by a signal recombination algorithm, where the removed window is automatically obtained by a derivative threshold. Secondly, the adaptive rational quartic Hermite interpolation algorithm is used to interpolate the removed window. Finally, the performance of the algorithm is verified using simulated and public database data. Main results. The simulation results show that the proposed algorithm improves the SNR by 23.88%–47.60%, reduces the RMSE by 46.52%–81.11%, reduces the average MAE by 47.83%–58.33%, and reduces the time consumption of the proposed algorithm by 45.90% compared with the piecewise cubic Hermite interpolation algorithm. Significance. Therefore, TMS-EEG pulse artifacts can be removed effectively and quickly with the proposed algorithm.

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.

Electroencephalography-functional magnetic resonance imaging of epileptiform discharges: Noninvasive investigation of the whole brain.

Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) is a unique and noninvasive method for investigating epileptic activity. Interictal epileptiform discharge-related EEG-fMRI provides cortical and subcortical blood oxygen level-dependent (BOLD) signal changes specific to epileptic discharges. As a result, EEG-fMRI has revealed insights into generators and networks involved in epileptic activity in different types of epilepsy, demonstrating-for instance-the implication of the thalamus in human generalized spike and wave discharges and the role of the default mode network in absences and focal epilepsy, and has suggested a mechanism for the cortico-subcortical interactions in Lennox-Gastaut syndrome discharges. EEG-fMRI can find deep sources of epileptic activity not available to scalp EEG or magnetoencephalography, and provides critical new information to delineate the epileptic focus when considering surgical treatment or electrode implantation. In recent years, methodological advances, such as artifact removal and automatic detection of events, have rendered this method easier to implement, and its clinical potential has since been established by evidence of the impact of BOLD response on clinical decision-making and of the relationship between concordance of BOLD responses with extent of resection and surgical outcome. This review presents the recent developments in EEG-fMRI methodology and EEG-fMRI studies in different types of epileptic disorders as follows: EEG-fMRI acquisition, gradient and pulse artifact removal, statistical analysis, clinical applications, presurgical evaluation, altered physiological state in generalized genetic epilepsy, and pediatric EEG-fMRI studies.

Spatial Filtering of Electroencephalography Reduces Artifacts and Enhances Signals Related to Spinal Cord Stimulation (SCS)

How spinal cord stimulation (SCS) in its different modes suppresses pain is poorly understood. Mechanisms of action may reside locally in the spinal cord, but also involve a larger network including subcortical and cortical brain structures. Tonic, burst, and high-frequency modes of SCS can, in principle, entrain distinct temporal activity patterns in this network, but finally have to yield specific effects on pain suppression. Here, we employ high-density electroencephalography (EEG) and recently developed spatial filtering techniques to reduce SCS artifacts and to enhance EEG signals specifically related to neuromodulation by SCS. We recorded high-density resting-state EEGs in patients suffering from pain of various etiologies under different modes of SCS. We established a pipeline for the robust spectral analysis of oscillatory brain activity during SCS, which includes spatial filtering for attenuation of pulse artifacts and enhancement of brain activity potentially modulated by SCS. In sensor regions responsive to SCS, neuromodulation strongly reduced activity in the theta and low alpha range (6-10 Hz) in all SCS modes. Results were consistent in all patients, and in accordance with thalamocortical dysrhythmia hypothesis of pain. Only in the tonic mode showing paresthesia as side effect, SCS also consistently and strongly reduced high-gamma activity (>84Hz). EEG spectral analysis combined with spatial filtering allows for a spatially and temporally specific assessment of SCS-related, neuromodulatory EEG activity, and may help to disentangle therapeutic and side effects of SCS.

Motion correction for IRT imaging in neurosurgery: Analysis and comparison of frequency-/filter- and intensity-based approaches

In neurosurgery, the patient’s pulse and breathing motion, as well as technical and physiological artifacts during Infrared Thermography (IRT) acquisition cause difficulties; accordingly, a robust and adjusted motion correction method is required. In this paper, a comparison of frequency-/filter- and intensity-based approaches for the Horn-Schunck method, the Lucas-Kanade method, the OA-CLG method, the phase-based method, and FIR bandstop filter is provided. Firstly, images are registered with respect to a reference image, and image enhancement as a preprocessing step is carried out for those methods that rely on brightness constancy assumption (BCA) only. In the second step, motion estimation and compensation for local motion are applied to suppress small motion, i.e., pulse and breathing motion correction. The processing step can be done either with the frequency-/filter- or intensity-based approaches. Comparisons are performed using three types of datasets namely, the human IRT brain data (clinical cases), semi-synthetic IRT brain data, and phantom IRT data. Results from semi-synthetic and phantom IRT data indicate that the intensity-based methods are able to estimate and compensate pulse and breathing motion artifacts while preserving all the image details, structures, and spatial resolution after motion correction. The human brain datasets demonstrate that motion correction can be a beneficial step in IRT imaging during neurosurgery, obtaining better correction metric-wise in each four performance measurements.

Improving intraoperative evoked potentials at short latency by a novel neuro-stimulation technology with delayed return discharge

ObjectiveThe intraoperative monitoring of cranial nerve function records evoked responses at latencies of a few milliseconds. Unfortunately, these responses may be masked by the electrical artifact of the stimulation pulse. In electrical stimulation, the return discharge of the stimulation pulse significantly contributes to the width of the electrical artifact. MethodsWe have generated stimulation pulses with an ISIS Neurostimulator (inomed Medizintechnik GmbH) providing a novel stimulation artifact reduction technique. It delays the return discharge of the stimulating pulse beyond the latency of the expected physiological response. This delayed return discharge is controlled such that no unintended physiological response is evoked. ResultsIn 21 neurosurgical interventions with motor evoked potentials of the facial nerve (FNMEP), the stimulation method generated a stimulation pulse artifact with reduced tail duration. Compared to conventional stimulation with immediate return discharge, the signal-to-noise ratio of the physiological response may improve with the novel stimulation method. In some surgeries, only the novel stimulation method generated clearly identifiable response signals. ConclusionsThe reduced width of the stimulation artifact extends the toolbox of intraoperative monitoring modalities by rendering the interpretation of cranial nerve evoked potentials more reliable. SignificanceThe novel technique enhances the number of patients for whom intraoperative monitoring may aid in cranial neurosurgery.

A Robust Beat-to-Beat Artifact Detection Algorithm for Pulse Wave

With the rise of the concept of smart cities and healthcare, artificial intelligence helps people pay increasing attention to the health of themselves. People can wear a variety of wearable devices to monitor their physiological conditions. The pulse wave is a kind of physiological signal which is widely applied in the physiological monitoring system. However, the pulse wave is susceptible to artifacts, which prevents its popularization. In this work, we propose a novel beat-to-beat artifact detection algorithm, which performs pulse wave segmentation based on wavelet transform and then detects artifacts beat by beat based on the decision list. We verified our method on data acquired from different databases and compared with experts’ annotations. The segmentation algorithm achieved an accuracy of 96.13%. When it is applied to detect main peaks, the performance achieved an accuracy of 99.11%. After the previous segmentation algorithm, the artifact detection algorithm can detect beat-to-beat pulse waves and artifacts with an accuracy of 98.11%. The result indicated that the proposed method is robust for pulse waves of different patterns and could effectively detect the artifact without the complex algorithm. In summary, our proposed algorithm is capable of annotating pulse waves of various patterns and determining pulse wave quality. Since our method is developed and evaluated on the transmission-mode PPG data, it is more suitable for the devices and applications inside the hospitals instead of reflectance-mode PPG.

Application of Stockwell Transform and Shannon Energy for Pace Pulses Detection in a Single-Lead ECG Corrupted by EMG Artifacts

Electrocardiogram (ECG) analysis is important for the detection of pace pulse artifacts, since their existence indicates the presence of a pacemaker. ECG gives information on the proper functionality of the device and could help to evaluate the reaction of the heart. Beyond the challenges related to the diversity of ECG arrhythmias and pace pulses, the existence of electromyogram (EMG) noise could cause serious problems for the correct detection of pace pulses. This study reveals the potential of a methodology based on Stockwell transformation (S-transform), subsequent Shannon energy calculation and a threshold-based rule for pace artifact detection in a single-lead ECG corrupted with EMG noise. The design, validation and test are performed on a large, publicly available artificial database acquired with high amplitude and time resolution. It includes various combinations of ECG arrhythmias and pace pulses with different amplitudes, rising edges and total pulse durations, as well as timing that corresponds to different pacemaker modes. The training was done over 312 (ECG + EMG) signals. The method was validated on 390 clean ECGs and independently tested on 312 (ECG + EMG) and 390 clean ECGs. The achieved accuracy over the test dataset was Se = 100%, PPV = 98.0% for ECG corrupted by EMG artifacts and Se = 99.9%, PPV = 98.3% for clean ECG signals. This shows that, despite EMG artifacts, the S-transform could distinctly localize the pace pulse positions and, together with the applied ShE, could provide precise pace pulses detection in the time domain.

Heartbeat-evoked cortical responses: Underlying mechanisms, functional roles, and methodological considerations

The heart continuously and cyclically communicates with the brain. Beyond homeostatic regulation and sensing, recent neuroscience research has started to shed light on brain-heart interactions in diverse cognitive and emotional processes. In particular, neural responses to heartbeats, as measured with the so-called heartbeat-evoked potential, have been shown to be useful for investigating cortical activity processing cardiac signals. In this review, we first overview and discuss the basic properties of the HEP such as underlying physiological pathways, brain regions, and neural mechanisms. We then provide a systematic review of the mental processes associated with cortical HEP activations, notably heartbeat perception, emotional feelings, perceptual awareness, and self-consciousness, in healthy subjects and clinical populations. Finally, we discuss methodological issues regarding the experimental design and data analysis for separating genuine HEP components from physiological artifacts (e.g., cardiac field artifact, pulse artifact) or other neural activities that are not specifically associated with the heartbeat. Findings from this review suggest that when intrinsic limitations (e.g., artifacts) are carefully controlled, the HEP could provide a reliable neural measure for investigating brain-viscera interactions in diverse mental processes.

Investigating the variability of cardiac pulse artifacts across heartbeats in simultaneous EEG-fMRI recordings: A 7T study

Electroencephalography (EEG) recordings performed in magnetic resonance imaging (MRI) scanners are affected by complex artifacts caused by heart function, often termed pulse artifacts (PAs). PAs can strongly compromise EEG data quality, and remain an open problem for EEG-fMRI. This study investigated the properties and mechanisms of PA variability across heartbeats, which has remained largely unaddressed to date, and evaluated its impact on PA correction approaches. Simultaneous EEG-fMRI was performed at 7T on healthy participants at rest or under visual stimulation, with concurrent recordings of breathing and cardiac activity. PA variability was found to contribute to EEG variance with more than 500 μV2 at 7T, which extrapolates to 92 μV2 at 3T. Clustering analyses revealed that PA variability not only is linked to variations in head position/orientation, as previously hypothesized, but also, and more importantly, to the respiratory cycle and to heart rate fluctuations. The latter mechanisms are associated to short-timescale variability (even across consecutive heartbeats), and their importance varied across EEG channels. In light of this PA variability, three PA correction techniques were compared: average artifact subtraction (AAS), optimal basis sets (OBS), and an approach based on K-means clustering. All methods allowed the recovery of visual evoked potentials from the EEG data; nonetheless, OBS and K-means tended to outperform AAS, likely due to the inability of the latter in modeling short-timescale variability. Altogether, these results offer novel insights into the dynamics and underlying mechanisms of the pulse artifact, with important consequences for its correction, relevant to most EEG-fMRI applications.

A Study on the Feasibility of the Deep Brain Stimulation (DBS) Electrode Localization Based on Scalp Electric Potential Recordings.

Deep Brain Stimulation (DBS) is an effective therapy for patients disabling motor symptoms from Parkinson’s disease, essential tremor, and other motor disorders. Precise, individualized placement of DBS electrodes is a key contributor to clinical outcomes following surgery. Electroencephalography (EEG) is widely used to identify the sources of intracerebral signals from the potential on the scalp. EEG is portable, non-invasive, low-cost, and it could be easily integrated into the intraoperative or ambulatory environment for localization of either the DBS electrode or evoked potentials triggered by stimulation itself. In this work, we studied with numerical simulations the principle of extracting the DBS electrical pulse from the patient’s EEG – which normally constitutes an artifact – and localizing the source of the artifact (i.e., the DBS electrodes) using EEG localization methods. A high-resolution electromagnetic head model was used to simulate the EEG potential at the scalp generated by the DBS pulse artifact. The potential distribution on the scalp was then sampled at the 256 electrode locations of a high-density EEG Net. The electric potential was modeled by a dipole source created by a given pair of active DBS electrodes. The dynamic Statistical Parametric Maps (dSPM) algorithm was used to solve the EEG inverse problem, and it allowed localization of the position of the stimulus dipole in three DBS electrode bipolar configurations with a maximum error of 1.5 cm. To assess the accuracy of the computational model, the results of the simulation were compared with the electric artifact amplitudes over 16 EEG electrodes measured in five patients. EEG artifacts measured in patients confirmed that simulated data are commensurate to patients’ data (0 ± 6.6 μV). While we acknowledge that further work is necessary to achieve a higher accuracy needed for surgical navigation, the results presented in this study are proposed as the first step toward a validated computational framework that could be used for non-invasive localization not only of the DBS system but also brain rhythms triggered by stimulation at both proximal and distal sites in the human central nervous system.

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.

Cause of Pulse Artefacts Inherent to the Electrodes of Neuromodulation Implants.

The current pulses delivered through platinum electrodes by medical implants to recruit neurons give rise to slowly decaying voltage tails, called "artefacts." These tails make measurement of evoked potentials following the pulses very difficult. We present an evidence to show that in a typical clinical scenario, these tails are mostly caused by the concentration gradients of species induced in the electrical double layer adsorbed onto the surface of both stimulating and passive electrodes. A compact model is presented that allows the simulation of these artefacts. The model is verified against measurements made in saline. This shows that electrode artefacts are an intrinsic property of the conductive electrodes of a lead.

The Effects of Resistance Exercise on Intracranial Pressure

IntroductionOne of the primary health concerns faced by NASA astronauts on board the International Space Station is a decline in visual acuity. It is believed that increases in intracranial pressure (ICP) due to resistive exercise performed on the station's Advanced Resistive Exercise Device (ARED) may contribute to Spaceflight Associated Neuro‐Ocular Syndrome (SANS), also referred to as the Visual Impairment and Intracranial Pressure (VIIP) syndrome. In a manner similar to low level Valsalva exertion, it is hypothesized that ICP would increase during low level resistance exercise.MethodsSix healthy subjects (4 male, 2 female, age: 27 ± 10 years, weight: 77 ± 15 kg) were recruited to perform low‐level resistance exercises in both the seated and supine positions. A Cerebral Cochlear Fluid Pressure analyzer (CCFP) was used to noninvasively measure ICP by assessing tympanic membrane displacement (Vm) in the inner ear following an auditory stimulus. Increased values of Vm correspond to a decrease in ICP. After a 10‐minute acclimation period, a baseline measurement was taken with the subject at rest in the seated position. ICP was then measured during randomized, isometric, overhead shoulder holds with weights of 0lbs, 2lbs, and 5lbs (per hand). This procedure was then repeated in the supine position. ICP was also measured in the 15‐degree head down tilt (HDT) position following a 10‐minute acclimation period. Significant differences between groups were determined at p<0.05 using a Student's T‐test.ResultsThe relative changes in Vm with increased loading by position are shown in Figure 1. Vm decreased by 40% ± 15% (p=0.001) in the HDT position as compared to the supine baseline, showing a predicted, reliable increase in ICP for positional changes. In the seated position, displacement increased slightly from baseline (12% ± 63%, p=0.412) for all subjects across all weight levels, indicating a statistically nonsignificant decrease in ICP. Measurements in the supine position were more consistent, with decreasing Vm (3% ± 3%, p=0.563) for all subjects across all weight levels, indicating a nonsignificant increase in ICP.DiscussionContrary to our hypothesis, measured data showed no significant change in ICP during low level resistance exercise in either the seated or supine positions. Based on these results, resistance exercise may not be the primary contributing factor to the development of SANS in astronauts. This study will form the basis of future investigation into the effects of exercise and ICP in different such as gender, age, and clinical populations. Sensitivity of the CCFP to motion and pulse artifacts will also be addressed to reduce variability between subjects.Support or Funding InformationSupported by NASA grant NNX13AJ12GThis abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

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.

Physiological noise reduction algorithms for fMRI data

This article describes various approaches for excluding and recognizing artifacts in functional magnetic resonance data. Similar methods can be applied to intractable artifacts, such as breathing and pulse artifacts, from white matter and cerebrospinal fluid pulsations. In this paper, using real experimental data various algorithms and approaches that can be applied not only to standard echo-planar mri-sequences, but also to ultra fast sequences are shown. Such methods make it possible for researchers to reveal only the actual human brain activity and more accurately talk about zones of functionality for certain cognitive tasks.