Removing ballistocardiogram (BCG) artifact from full-scalp EEG acquired inside the MR scanner with Orthogonal Matching Pursuit (OMP).
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ABSTRACT:
SUBMITTER: Xia H
PROVIDER: S-EPMC4114198 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
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Project description:ObjectiveThe concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG. This is not ideal as the BCG signal propagates in a manner which is non-linearly dependent on the electrocardiogram (ECG). In this paper, we present a novel method for BCG artifact suppression using recurrent neural networks (RNNs).MethodsEEG signals were recovered by training RNNs on the nonlinear mappings between ECG and the BCG corrupted EEG. We evaluated our model's performance against the commonly used Optimal Basis Set (OBS) method at the level of individual subjects, and investigated generalization across subjects.ResultsWe show that our algorithm can generate larger average power reduction of the BCG at critical frequencies, while simultaneously improving task relevant EEG based classification.ConclusionThe presented deep learning architecture can be used to reduce BCG related artifacts in EEG-fMRI recordings.SignificanceWe present a deep learning approach that can be used to suppress the BCG artifact in EEG-fMRI without the use of additional hardware. This method may have scope to be combined with current hardware methods, operate in real-time and be used for direct modeling of the BCG.
| S-EPMC7808341 | biostudies-literature
Project description:In simultaneous EEG-fMRI, the EEG recordings are severely contaminated by ballistocardiogram (BCG) artifacts, which are caused by cardiac pulsations. To reconstruct and remove the BCG artifacts, one promising method is to measure the artifacts in the absence of EEG signal by placing a group of electrodes (BCG electrodes) on a conductive layer (reference layer) insulated from the scalp. However, current BCG reference layer (BRL) methods either use a customized EEG cap composed of electrode pairs, or need to construct the custom reference layer through additional model-building experiments for each EEG-fMRI experiment. These requirements have limited the versatility and efficiency of BRL. The aim of this study is to propose a more practical and efficient BRL method and compare its performance with the most popular BCG removal method, the optimal basis sets (OBS) algorithm.By designing the reference layer as a permanent and reusable cap, the new BRL method is able to be used with a standard EEG cap, and no extra experiments and preparations are needed to use the BRL in an EEG-fMRI experiment.The BRL method effectively removed the BCG artifacts from both oscillatory and evoked potential scalp recordings and recovered the EEG signal.Compared to the OBS, this new BRL method improved the contrast-to-noise ratios of the alpha-wave, visual, and auditory evoked potential signals by 101%, 76%, and 75%, respectively, employing 160 BCG electrodes. Using only 20 BCG electrodes, the BRL improved the EEG signal by 74%/26%/41%, respectively.The proposed method can substantially improve the EEG signal quality compared with traditional methods.
| S-EPMC4126606 | biostudies-literature
Project description:Muscle electrical activity, or "electromyogenic" (EMG) artifact, poses a serious threat to the validity of electroencephalography (EEG) investigations in the frequency domain. EMG is sensitive to a variety of psychological processes and can mask genuine effects or masquerade as legitimate neurogenic effects across the scalp in frequencies at least as low as the alpha band (8-13 Hz). Although several techniques for correcting myogenic activity have been described, most are subjected to only limited validation attempts. Attempts to gauge the impact of EMG correction on intracerebral source models (source "localization" analyses) are rarer still. Accordingly, we assessed the sensitivity and specificity of one prominent correction tool, independent component analysis (ICA), on the scalp and in the source-space using high-resolution EEG. Data were collected from 17 participants while neurogenic and myogenic activity was independently varied. Several protocols for classifying and discarding components classified as myogenic and non-myogenic artifact (e.g., ocular) were systematically assessed, leading to the exclusion of one-third to as much as three-quarters of the variance in the EEG. Some, but not all, of these protocols showed adequate performance on the scalp. Indeed, performance was superior to previously validated regression-based techniques. Nevertheless, ICA-based EMG correction exhibited low validity in the intracerebral source-space, likely owing to incomplete separation of neurogenic from myogenic sources. Taken with prior work, this indicates that EMG artifact can substantially distort estimates of intracerebral spectral activity. Neither regression- nor ICA-based EMG correction techniques provide complete safeguards against such distortions. In light of these results, several practical suggestions and recommendations are made for intelligently using ICA to minimize EMG and other common artifacts.
| S-EPMC2818255 | biostudies-literature
Project description:The high-speed atomic force microscopy (HS-AFM) is a unique and prominent method to observe structural dynamics of biomolecules at single molecule level at near-physiological condition. To achieve high temporal resolution, the probe tip scans the stage at high speed which can cause the so-called parachuting artifact in the HS-AFM images. Here, we develop a computational method to detect and remove the parachuting artifact in HS-AFM images using the two-way scanning data. To merge the two-way scanning images, we employed a method to infer the piezo hysteresis effect and to align the forward- and backward-scanning images. We then tested our method for HS-AFM videos of actin filaments, molecular chaperone, and duplex DNA. Together, our method can remove the parachuting artifact from the raw HS-AFM video containing two-way scanning data and make the processed video free from the parachuting artifact. The method is general and fast so that it can easily be applied to any HS-AFM videos with two-way scanning data.
| S-EPMC10205583 | biostudies-literature
Project description:The viability of building artificial metabolic pathways within a cell will depend on our ability to design biocompatible and orthogonal catalysts capable of achieving non-natural transformations. In this context, transition metal complexes offer unique possibilities to develop catalytic reactions that do not occur in nature. However, translating the potential of metal catalysts to living cells poses numerous challenges associated to their biocompatibility, and their stability and reactivity in crowded aqueous environments. Here we report a gold-mediated C-C bond formation that occurs in complex aqueous habitats, and demonstrate that the reaction can be translated to living mammalian cells. Key to the success of the process is the use of designed, water-activatable gold chloride complexes. Moreover, we demonstrate the viability of achieving the gold-promoted process in parallel with a ruthenium-mediated reaction, inside living cells, and in a bioorthogonal and mutually orthogonal manner.
| S-EPMC5954130 | biostudies-literature
Project description:EEG recordings made during concurrent fMRI are confounded by the pulse artefact (PA), which although smaller than the gradient artefact is often more problematic because of its variability over multiple cardiac cycles. A better understanding of the PA is needed in order to generate improved methods for reducing its effect in EEG-fMRI experiments. Here we performed a study aimed at identifying the relative contributions of three putative sources of the PA (cardiac-pulse-driven head rotation, the Hall effect due to pulsatile blood flow and pulse-driven expansion of the scalp) to its amplitude and variability. EEG recordings were made from 6 subjects lying in a 3T scanner. Accelerometers were fixed on the forehead and temple to monitor head motion. A bite-bar and vacuum cushion were used to restrain the head, thus greatly attenuating the contribution of cardiac-driven head rotation to the PA, while an insulating layer placed between the head and the EEG electrodes was used to eliminate the Hall voltage contribution. Using the root mean square (RMS) amplitude of the PA averaged over leads and time as a measure of the PA amplitude, we found that head restraint and insulating layer reduced the PA by 61% and 42%, respectively, when compared with the PA induced with the subject relaxed, indicating that cardiac-pulse-driven head rotation is the dominant source of the PA. With both the insulating layer and head restraint in place, the PA was reduced in RMS amplitude by 78% compared with the relaxed condition, the remaining PA contribution resulting from scalp expansion or residual head motion. The variance of the PA across cardiac cycles was more strongly reduced by the insulating layer than the head restraint, indicating that the flow-induced Hall voltage makes a larger contribution than pulse-driven head rotation to the variability of the PA.
| S-EPMC3601330 | biostudies-literature
Project description:Subendocardial dark-rim artifacts (DRAs) remain a major concern in first-pass perfusion (FPP) myocardial MRI and may lower the diagnostic accuracy for detection of ischemia. A major source of DRAs is the "Gibbs ringing" effect. We propose an optimized radial acquisition strategy aimed at eliminating ringing-induced DRAs in FPP.By studying the underlying point spread function (PSF), we show that optimized radial sampling with a simple reconstruction method can eliminate the oscillations in the PSF that cause ringing artifacts. We conducted realistic MRI phantom experiments and in vivo studies (n?=?12 healthy humans) to evaluate the artifact behavior of the proposed imaging scheme in comparison to a conventional Cartesian imaging protocol.Simulations and phantom experiments verified our theoretical expectations. The in vivo studies showed that optimized radial imaging is capable of significantly reducing DRAs in the early myocardial enhancement phase (during which the ringing effect is most prominent and may obscure perfusion defects) while providing similar resolution and image quality compared with conventional Cartesian imaging.The developed technical framework and results demonstrate that, in comparison to conventional Cartesian techniques, optimized radial imaging with the proposed optimizations significantly reduces the prevalence and spatial extent of DRAs in FPP imaging.
| S-EPMC4176898 | biostudies-literature
Project description:BackgroundSleep Apnea Syndrome (SAS) is a common sleep-related breathing disorder, which affects about 4-7% males and 2-4% females all around the world. Different approaches have been adopted to diagnose SAS and measure its severity, including the gold standard Polysomnography (PSG) in sleep study field as well as several alternative techniques such as single-channel ECG, pulse oximeter and so on. However, many shortcomings still limit their generalization in home environment. In this study, we aim to propose an efficient approach to automatically assess the severity of sleep apnea syndrome based on the ballistocardiogram (BCG) signal, which is non-intrusive and suitable for in home environment.MethodsWe develop an unobtrusive sleep monitoring system to capture the BCG signals, based on which we put forward a three-stage sleep apnea syndrome severity assessment framework, i.e., data preprocessing, sleep-related breathing events (SBEs) detection, and sleep apnea syndrome severity evaluation. First, in the data preprocessing stage, to overcome the limits of BCG signals (e.g., low precision and reliability), we utilize wavelet decomposition to obtain the outline information of heartbeats, and apply a RR correction algorithm to handle missing or spurious RR intervals. Afterwards, in the event detection stage, we propose an automatic sleep-related breathing event detection algorithm named Physio_ICSS based on the iterative cumulative sums of squares (i.e., the ICSS algorithm), which is originally used to detect structural breakpoints in a time series. In particular, to efficiently detect sleep-related breathing events in the obtained time series of RR intervals, the proposed algorithm not only explores the practical factors of sleep-related breathing events (e.g., the limit of lasting duration and possible occurrence sleep stages) but also overcomes the event segmentation issue (e.g., equal-length segmentation method might divide one sleep-related breathing event into different fragments and lead to incorrect results) of existing approaches. Finally, by fusing features extracted from multiple domains, we can identify sleep-related breathing events and assess the severity level of sleep apnea syndrome effectively.ConclusionsExperimental results on 136 individuals of different sleep apnea syndrome severities validate the effectiveness of the proposed framework, with the accuracy of 94.12% (128/136).
| S-EPMC5405918 | biostudies-literature
Project description:This paper explores the homogeneity of coefficients in high-dimensional regression, which extends the sparsity concept and is more general and suitable for many applications. Homogeneity arises when regression coefficients corresponding to neighboring geographical regions or a similar cluster of covariates are expected to be approximately the same. Sparsity corresponds to a special case of homogeneity with a large cluster of known atom zero. In this article, we propose a new method called clustering algorithm in regression via data-driven segmentation (CARDS) to explore homogeneity. New mathematics are provided on the gain that can be achieved by exploring homogeneity. Statistical properties of two versions of CARDS are analyzed. In particular, the asymptotic normality of our proposed CARDS estimator is established, which reveals better estimation accuracy for homogeneous parameters than that without homogeneity exploration. When our methods are combined with sparsity exploration, further efficiency can be achieved beyond the exploration of sparsity alone. This provides additional insights into the power of exploring low-dimensional structures in high-dimensional regression: homogeneity and sparsity. Our results also shed lights on the properties of the fussed Lasso. The newly developed method is further illustrated by simulation studies and applications to real data. Supplementary materials for this article are available online.
| S-EPMC4465377 | biostudies-literature
