Showing papers on "Artifact (error) published in 2013"
TL;DR: It is demonstrated that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed.
1,448 citations
TL;DR: This artifact was described in cohorts of children, adolescents, and adults, and its severity (magnitude) was related to the prevalence of motion within a cohort, which is a substantial confound in the examination of single rs-fcMRI datasets and in comparisons of multiple rs-FCMRI datasets.
308 citations
TL;DR: This paper describes a novel artifact removal technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) which is capable of operating on single-channel measurements and is shown to produce significantly improved results.
Abstract: Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source, then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are, however, considerably fewer techniques available if only a single-channel measurement is available and yet single-channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single-channel measurements. The EEMD technique is first used to decompose the single-channel signal into a multidimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second-order statistics. The new technique is tested against the currently available wavelet denoising and EEMD-ICA techniques using both electroencephalography and functional near-infrared spectroscopy data and is shown to produce significantly improved results.
234 citations
TL;DR: This work investigates the different online and offline methods for reducing artifacts in TMS–EEG recordings and the physiological information contained within the TMS‐evoked cortical response, and addresses the use of TMS-EEG to assess different cortical mechanisms such as cortical inhibition and neural plasticity.
Abstract: The past decade has seen significant developments in the concurrent use of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to directly assess cortical network properties such as excitability and connectivity in humans. New hardware solutions, improved EEG amplifier technology, and advanced data processing techniques have allowed substantial reduction of the TMS-induced artifact, which had previously rendered concurrent TMS-EEG impossible. Various physiological artifacts resulting from TMS have also been identified, and methods are being developed to either minimize or remove these sources of artifact. With these developments, TMS-EEG has unlocked regions of the cortex to researchers that were previously inaccessible to TMS. By recording the TMS-evoked response directly from the cortex, TMS-EEG provides information on the excitability, effective connectivity, and oscillatory tuning of a given cortical area, removing the need to infer such measurements from indirect measures. In the following review, we investigate the different online and offline methods for reducing artifacts in TMS-EEG recordings and the physiological information contained within the TMS-evoked cortical response. We then address the use of TMS-EEG to assess different cortical mechanisms such as cortical inhibition and neural plasticity, before briefly reviewing studies that have utilized TMS-EEG to explore cortical network properties at rest and during different functional brain states.
206 citations
TL;DR: The authors examined whether existing estimates of network size and social isolation, drawn from egocentric name generators across several representative samples, suffer from systematic biases and showed that they are not robust enough.
Abstract: This article examines whether existing estimates of network size and social isolation, drawn from egocentric name generators across several representative samples, suffer from systematic biases lin...
170 citations
TL;DR: Most CBCT devices performed worse than MSCT for titanium artifacts, but all of them performed better for lead artifacts, and no clear improvement of metal artifacts was seen for high-dose protocols, although certain devices showed some artifact reduction for large FOV or high exposure protocols.
Abstract: Objectives To quantify metal artifacts obtained from a wide range of cone beam computed tomography (CBCT) devices and exposure protocols, to compare their tolerance to metals of different densities, and to provide insights regarding the possible implementation of metal artifact analysis into a QC protocol for CBCT. Materials and methods A customized polymethyl methacrylate (PMMA) phantom, containing titanium and lead rods, was fabricated. It was scanned on 13 CBCT devices and one multi-slice computed tomography (MSCT) device, including high-dose and low-dose exposure protocols. Artifacts from the rods were assessed by two observers by measuring the standard deviation of voxel values in the vicinity of the rods, and normalizing this value to the percentage of the theoretical maximum standard deviation. Results For CBCT datasets, artifact values ranged between 6.1% and 27.4% for titanium, and between 10.% and 43.7% for lead. Most CBCT devices performed worse than MSCT for titanium artifacts, but all of them performed better for lead artifacts. In general, no clear improvement of metal artifacts was seen for high-dose protocols, although certain devices showed some artifact reduction for large FOV or high exposure protocols. Conclusions Regions in the vicinity of the metal rods were moderately or gravely affected, particularly in the area between the rods. In practice, the CBCT user has very limited possibilities to reduce artifacts. Researchers and manufacturers need to combine their efforts in optimizing exposure factors and implementing metal artifact reduction algorithms.
169 citations
TL;DR: A method of compensating the motion artifact is demonstrated, the results of which emphasize the need for surface motion compensation when measuring the mechanical response for elastography or other biomedical applications.
Abstract: We describe theoretical and experimental investigations of motion artifacts that can arise in the detection of shear wave propagating within tissue with phase-sensitive optical coherence tomography. We find that the motion artifact is a combined product of sample surface motion and refractive index difference between sample and air, which cannot be neglected when estimating the tissue motion within tissue. A method of compensating the motion artifact is demonstrated, the results of which emphasize the need for surface motion compensation when measuring the mechanical response for elastography or other biomedical applications.
106 citations
TL;DR: The secondary artifact observed in humans is consistent with activation of scalp muscles following T MS, and can be recorded within a short period of time following TMS, however measures must be taken to avoid muscle stimulation.
98 citations
TL;DR: In this paper, the effects of the pressure exerted onto the electrode, and the effect of inserting padding between the applied pressure and the electrode were investigated, and guidelines for improving electrode design regarding padding and pressure can be formulated as paddings are a necessary part of the system for reducing the motion artifact, and further, their effect maximises between 15 and 20 mmHg of exerted pressure.
Abstract: With the aging population and rising healthcare costs, wearable monitoring is gaining importance. The motion artifact affecting dry electrodes is one of the main challenges preventing the widespread use of wearable monitoring systems. In this paper we investigate the motion artifact and ways of making a textile electrode more resilient against motion artifact. Our aim is to study the effects of the pressure exerted onto the electrode, and the effects of inserting padding between the applied pressure and the electrode. We measure real time electrode-skin interface impedance, ECG from two channels, the motion artifact related surface potential, and exerted pressure during controlled motion by a measurement setup designed to estimate the relation of motion artifact to the signals. We use different foam padding materials with various mechanical properties and apply electrode pressures between 5 and 25 mmHg to understand their effect. A QRS and noise detection algorithm based on a modified Pan-Tompkins QRS detection algorithm estimates the electrode behaviour in respect to the motion artifact from two channels; one dominated by the motion artifact and one containing both the motion artifact and the ECG. This procedure enables us to quantify a given setup’s susceptibility to the motion artifact. Pressure is found to strongly affect signal quality as is the use of padding. In general, the paddings reduce the motion artifact. However the shape and frequency components of the motion artifact vary for different paddings, and their material and physical properties. Electrode impedance at 100 kHz correlates in some cases with the motion artifact but it is not a good predictor of the motion artifact. From the results of this study, guidelines for improving electrode design regarding padding and pressure can be formulated as paddings are a necessary part of the system for reducing the motion artifact, and further, their effect maximises between 15 mmHg and 20 mmHg of exerted pressure. In addition, we present new methods for evaluating electrode sensitivity to motion, utilizing the detection of noise peaks that fall into the same frequency band as R-peaks.
96 citations
TL;DR: A new adaptive FLN-RBFN-based filter is proposed to cancel the three most serious contaminants, i.e. ocular, muscular and cardiac artifacts from EEG signal, demonstrating the effectiveness of the proposed technique in extracting the desired EEG component from contaminated EEG signal.
81 citations
TL;DR: This paper proposes an efficient compressed sensing-based algorithm for CT image reconstruction from few-view data where the main feature is the use of two sparsity transforms—discrete wavelet transform and discrete gradient transform.
Abstract: In computed tomography (CT), there are many situations where reconstruction has to be performed with sparse-view data. In sparse-view CT imaging, strong streak artifacts may appear in conventionally reconstructed images due to limited sampling rate that compromises image quality. Compressed sensing (CS) algorithm has shown potential to accurately recover images from highly undersampled data. In the past few years, total-variation-(TV-) based compressed sensing algorithms have been proposed to suppress the streak artifact in CT image reconstruction. In this paper, we propose an efficient compressed sensing-based algorithm for CT image reconstruction from few-view data where we simultaneously minimize three parameters: the norm, total variation, and a least squares measure. The main feature of our algorithm is the use of two sparsity transforms—discrete wavelet transform and discrete gradient transform. Experiments have been conducted using simulated phantoms and clinical data to evaluate the performance of the proposed algorithm. The results using the proposed scheme show much smaller streaking artifacts and reconstruction errors than other conventional methods.
TL;DR: The experimental results on both the artificially contaminated EEG data and publicly available real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly non-stationary and the underlying sources cannot be assumed to be independent or uncorrelated.
Abstract: Ocular contamination of EEG data is an important and very common problem in the diagnosis of neurobiological events. An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. First, it conducts the blind source separation on the raw EEG recording by the stationary subspace analysis, which can concentrate artifacts in fewer components than the representative blind source separation methods. Next, to recover the neural information that has leaked into the artifactual components, the adaptive signal decomposition technique EMD is applied to denoise the components. Finally, the artifact-only components are projected back to be subtracted from EEG signals to get the clean EEG data. The experimental results on both the artificially contaminated EEG data and publicly available real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly non-stationary and the underlying sources cannot be assumed to be independent or uncorrelated.
TL;DR: This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction.
Abstract: Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.
TL;DR: This work describes another systematic artifact in the EEG signal, which is induced by the internal ventilation system of Siemens TRIO and VERIO MR scanners.
TL;DR: The model-based motion estimation method is nearly insensitive to image artifacts of gated 4D reconstructions as they are caused by angular undersampling and shows almost no sensitivity to sparse-view artifacts and thus ensures both high spatial and high temporal resolution.
Abstract: Purpose: In image-guided radiation therapy (IGRT) valuable information for patient positioning, dose verification, and adaptive treatment planning is provided by an additional kV imaging unit. However, due to the limited gantry rotation speed during treatment the typical acquisition time is quite long. Tomographic images of the thorax suffer from motion blurring or, if a gated 4D reconstruction is performed, from significant streak artifacts. Our purpose is to provide a method that reliably estimates respiratory motion in presence of severe artifacts. The estimated motion vector fields are then used for motion-compensated image reconstruction to provide high quality respiratory-correlated 4D volumes for on-board cone-beam CT (CBCT) scans. Methods: The proposed motion estimation method consists of a model that explicitly addresses image artifacts because in presence of severe artifacts state-of-the-art registration methods tend to register artifacts rather than anatomy. Our artifact model, e.g., generates streak artifacts very similar to those included in the gated 4D CBCT images, but it does not include respiratory motion. In combination with a registration strategy, the model gives an error estimate that is used to compensate the corresponding errors of the motion vector fields that are estimated from the gated 4D CBCT images. The algorithm is tested in combination with a cyclic registration approach using temporal constraints and with a standard 3D–3D registration approach. A qualitative and quantitative evaluation of the motion-compensated results was performed using simulated rawdata created on basis of clinical CT data. Further evaluation includes patient data which were scanned with an on-board CBCT system. Results: The model-based motion estimation method is nearly insensitive to image artifacts of gated 4D reconstructions as they are caused by angular undersampling. The motion is accurately estimated and our motion-compensated image reconstruction algorithm can correct for it. Motion artifacts of 3D standard reconstruction are significantly reduced, while almost no new artifacts are introduced. Conclusions: Using the artifact model allows to accurately estimate and compensate for patient motion, even if the initial reconstructions are of very low image quality. Using our approach together with a cyclic registration algorithm yields a combination which shows almost no sensitivity to sparse-view artifacts and thus ensures both high spatial and high temporal resolution.
23 Jan 2013
TL;DR: It is shown that PCA can be used to reduce TMS-induced artifacts in EEG, thereby revealing components of the TMS evoked potential.
Abstract: Co-registration of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is a new, promising method for assessing cortical excitability and connectivity. Using this technique, a TMS evoked potential (TEP) can be induced and registered with the EEG. However, the TEP contains an early, short lasting artifact due to the magnetic pulse, and a second artifact, which depends on the location of stimulation and can last up to 40 ms. Different causes for this second artifact have been suggested in literature. In this study, we used principal component analysis (PCA) to suppress both the first and second artifact in TMS-EEG data. Single pulse TMS was applied at the motor and visual cortex in 18 healthy subjects. PCA using singular value decomposition was applied on single trials to suppress the artifactual components. A large artifact suppression was realized after the removal of the first five PCA components, thereby revealing early TEP peaks, with only a small suppression of later TEP components. The spatial distribution of the second artifact suggests that it is caused by electrode movement due to activation of the temporal musculature. In conclusion, we showed that PCA can be used to reduce TMS-induced artifacts in EEG, thereby revealing components of the TMS evoked potential.
TL;DR: The large contributions of pure translational parameters and of non-linear terms to the BCG waveforms indicate that non-rigid motion of the EEG wires (originating from rigid head motion) is likely an important cause of the artifact.
23 Jan 2013
TL;DR: A multivariate automatic and adaptive method for identifying artifacts in continuous EEG data is presented and it is shown that artifacts management is a critical problem in any applications involving on-line processing of EEG signals.
Abstract: Artifacts management is a critical problem in any applications involving on-line processing of EEG signals. This paper presents a multivariate automatic and adaptive method for identifying artifacts in continuous EEG data.
TL;DR: An effective automated method is developed which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data.
Abstract: An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available.
TL;DR: DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series is developed, allowing researchers to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience.
Abstract: Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration.
TL;DR: This information may help users employ twinkling and B-mode to identify stones and developers to improve signal processing to harness the fundamental acoustic differences to ultimately improve stone detection.
Abstract: Purpose: To compare color Doppler twinkling artifact and B-mode ultrasonography in detecting kidney stones. Patients and Methods: Nine patients with recent CT scans prospectively underwent B-mode and twinkling artifact color Doppler ultrasonography on a commercial ultrasound machine. Video segments of the upper pole, interpolar area, and lower pole were created, randomized, and independently reviewed by three radiologists. Receiver operator characteristics were determined. Results: There were 32 stones in 18 kidneys with a mean stone size of 8.9±7.5 mm. B-mode ultrasonography had 71% sensitivity, 48% specificity, 52% positive predictive value, and 68% negative predictive value, while twinkling artifact Doppler ultrasonography had 56% sensitivity, 74% specificity, 62% positive predictive value, and 68% negative predictive value. Conclusions: When used alone, B-mode is more sensitive, but twinkling artifact is more specific in detecting kidney stones. This information may help users employ twinklin...
TL;DR: This study supports that RS-MRI is an adequate substitute that allows reduced use of CT imaging and resultant exposure to ionizing radiation in children with shunts and the potential for lifetime reduction in radiation exposure may justify this expense in children.
TL;DR: Although metal artifact reduction is not capable of fully removing artifacts caused by implants with high x-ray absorption, it is shown that the image quality of contrast-enhanced conebeam CT data are improved drastically.
Abstract: BACKGROUND AND PURPOSE: Developments in flat panel angiographic C-arm systems have enabled visualization of both the neurovascular stents and host arteries in great detail, providing complementary spatial information in addition to conventional DSA. However, the visibility of these structures may be impeded by artifacts generated by adjacent radio-attenuating objects. We report on the use of a metal artifact reduction algorithm for high-resolution contrast-enhanced conebeam CT for follow-up imaging of stent-assisted coil embolization. MATERIALS AND METHODS: Contrast-enhanced conebeam CT data were acquired in 25 patients who underwent stent-assisted coiling. Reconstructions were generated with and without metal artifact reduction and were reviewed by 3 experienced neuroradiologists by use of a 3-point scale. RESULTS: With metal artifact reduction, the observers agreed that the visibility had improved by at least 1 point on the scoring scale in >40% of the cases (κ = 0.6) and that the streak artifact was not obscuring surrounding structures in 64% of all cases (κ = 0.6). Metal artifact reduction improved the image quality, which allowed for visibility sufficient for evaluation in 65% of the cases, and was preferred over no metal artifact reduction in 92% (κ = 0.9). Significantly higher scores were given with metal artifact reduction (P CONCLUSIONS: Although metal artifact reduction is not capable of fully removing artifacts caused by implants with high x-ray absorption, we have shown that the image quality of contrast-enhanced conebeam CT data are improved drastically. The impact of the artifacts on the visibility varied between cases, and yet the overall visibility of the contrast-enhanced conebeam CT with metal artifact reduction improved in most the cases.
TL;DR: Compared with without artifact elimination, feature selection using a genetic algorithm (GA), feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.
Abstract: In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.
TL;DR: A blink restoration system for uni-lateral facial paralyzed patients is described and achieves restoration of synchronized blink through processing the myoelectric signal of orbicularis oculi at the normal side in real-time as the trigger to stimulate the paralyzed eyelid.
Abstract: Patients suffering from facial paralysis are on the hazard of disfigurement and loss of vision due to loss of blink function. Functional-electrical stimulation (FES) is one possible way of restoring blink and other functions in these patients. A blink restoration system for uni-lateral facial paralyzed patients is described in this paper. The system achieves restoration of synchronized blink through processing the myoelectric signal of orbicularis oculi at the normal side in real-time as the trigger to stimulate the paralyzed eyelid. Design issues are discussed, including EMG processing, stimulating strategies and real-time artifact blanking. Two artifact removal approaches based on sample and hold and digital filtering technique are proposed and implemented. Finally, the whole system has been verified on rabbit models.
25 May 2013
TL;DR: Computational assessment of corrected EEG waveforms reveals that the proposed algorithm retrieves the EEG data by removing the eye blink artifacts reliably and compared to other eye blink artifact removal techniques, the proposed method has two benefits.
Abstract: This research proposes a new hybrid algorithm for automatic removal of eye blink artifact from EEG data based on empirical mode decomposition (EMD) and canonical correlation analysis (CCA). The validity and efficiency of the proposed algorithm is evaluated using correlation coefficient and signal-to-artifact ratio (SAR) and the proposed algorithm is also compared with other popular eye blink artifact removal techniques (CCA, ICA, EMD-ICA) on simulated EEG data of two channels. From the simulation results, the average correlation coefficients for the EEG channels are obtained as 0.908 and 0.864 respectively. The SAR of the EEG signal also improved from 2.2 dB to 6.0 dB after correction using our proposed method. Compared to other eye blink artifact removal techniques, our proposed method has two benefits. Firstly, no visual inspection is required to detect the eye blink artifact components. Secondly, computational assessment of corrected EEG waveforms reveals that the proposed algorithm retrieves the EEG data by removing the eye blink artifacts reliably.
TL;DR: Substantial image quality improvements are possible with readout-segmented vs. single-shot EPI - the current clinical standard for DWI - regardless of field strength, which results in improved image quality secondary to greater real spatial resolution and reduced artifacts from susceptibility in MR imaging of the brain.
Abstract: BackgroundDiffusion-weighted imaging (DWI) magnetic resonance imaging (MRI) is most commonly performed utilizing a single-shot echo-planar imaging technique (ss-EPI). Susceptibility artifact and image blur are severe when this sequence is utilized at 3 T.PurposeTo evaluate a readout-segmented approach to DWI MR in comparison with single-shot echo planar imaging for brain MRI.Material and MethodsEleven healthy volunteers and 14 patients with acute and early subacute infarctions underwent DWI MR examinations at 1.5 and 3T with ss-EPI and readout-segmented echo-planar (rs-EPI) DWI at equal nominal spatial resolutions. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) calculations were made, and two blinded readers ranked the scans in terms of high signal intensity bulk susceptibility artifact, spatial distortions, image blur, overall preference, and motion artifact.ResultsSNR and CNR were greatest with rs-EPI (8.1±0.2 SNR vs. 6.0±0.2; P <10-4 at 3T).Spatial distortions were greater with single-sh...
TL;DR: The aim of the paper is to give an overview of the most common sources of noise and review methods for prevention and removal of noise in EEG recording, including elimination of noise sources.
Abstract: Electroencephalographic (EEG) recordings are often contaminated with several artifacts.Powerline interference and baseline noise is always present in EEG response of every patient. A number of strategies are available to deal with noise effectively both at the time of EEG recording as well as during preprocessing of recorded data. The aim of the paper is to give an overview of the most common sources of noise and review methods for prevention and removal of noise in EEG recording ,including elimination of noise sources.
TL;DR: A method based on correlation index and the feature of power distribution to automatically detect eye blink components and it is proved mathematically that the correlation between independent components and scalp EEG channels can be translating directly from the mixing matrix of ICA.
Abstract: Eye blink is an important and inevitable artifact during scalp electroencephalogram (EEG) recording. The main problem in EEG signal processing is how to identify eye blink components automatically with independent component analysis (ICA). Taking into account the fact that the eye blink as an external source has a higher sum of correlation with frontal EEG channels than all other sources due to both its location and significant amplitude, in this paper, we proposed a method based on correlation index and the feature of power distribution to automatically detect eye blink components. Furthermore, we prove mathematically that the correlation between independent components and scalp EEG channels can be translating directly from the mixing matrix of ICA. This helps to simplify calculations and understand the implications of the correlation. The proposed method doesn't need to select a template or thresholds in advance, and it works without simultaneously recording an electrooculography (EOG) reference. The experimental results demonstrate that the proposed method can automatically recognize eye blink components with a high accuracy on entire datasets from 15 subjects.