Showing papers on "Artifact (error) published in 2021"

A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach

Abstract: The electroencephalogram (EEG) signals are a big data which are frequently corrupted by motion artifacts. As human neural diseases, diagnosis and analysis need a robust neurological signal. Consequently, the EEG artifacts’ eradication is a vital step. In this research paper, the primary motion artifact is detected from a single-channel EEG signal using support vector machine (SVM) and preceded with further artifacts’ suppression. The signal features’ abstraction and further detection are done through ensemble empirical mode decomposition (EEMD) algorithm. Moreover, canonical correlation analysis (CCA) filtering approach is applied for motion artifact removal. Finally, leftover motion artifacts’ unpredictability is removed by applying wavelet transform (WT) algorithm. Finally, results are optimized by using Harris hawks optimization (HHO) algorithm. The results of the assessment confirm that the algorithm recommended is superior to the algorithms currently in use.

EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising.

Abstract: Objective.Deep learning (DL) networks are increasingly attracting attention across various fields, including electroencephalography (EEG) signal processing. These models provide comparable performance to that of traditional techniques. At present, however, there is a lack of well-structured and standardized datasets with specific benchmark limit the development of DL solutions for EEG denoising.Approach.Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing DL-based denoising models, as well as for performance comparisons across models. EEGdenoiseNet contains 4514 clean EEG segments, 3400 ocular artifact segments and 5598 muscular artifact segments, allowing users to synthesize contaminated EEG segments with the ground-truth clean EEG.Main results.We used EEGdenoiseNet to evaluate denoising performance of four classical networks (a fully-connected network, a simple and a complex convolution network, and a recurrent neural network). Our results suggested that DL methods have great potential for EEG denoising even under high noise contamination.Significance.Through EEGdenoiseNet, we hope to accelerate the development of the emerging field of DL-based EEG denoising. The dataset and code are available athttps://github.com/ncclabsustech/EEGdenoiseNet.

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

Abstract: Removal of motion artifacts is a critical challenge, especially in wearable electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed to daily movements. Recently, the significance of the motion artifact removal techniques has increased since EEG based brain-computer interfaces (BCI) and daily healthcare usage of wearable PPG devices were spotlighted. In this paper, the development on EEG and PPG sensor systems is introduced. Then, understanding of motion artifact and its reduction methods implemented by hardware and/or software fashions are reviewed. Various electrode types, analog readout circuits and signal processing techniques are studied for EEG motion artifact removal. In addition, recent in-ear EEG techniques with motion artifact reduction are also introduced. Furthermore, techniques compensating independent/dependent motion artifacts are presented for PPG.

Unsupervised Eye Blink Artifact Detection From EEG With Gaussian Mixture Model

Abstract: Eye blink is one of the most common artifacts in electroencephalogram (EEG) and significantly affects the performance of the EEG related applications, such as epilepsy recognition, spike detection, encephalitis diagnosis, etc. To achieve an accurate and efficient eye blink detection, a novel unsupervised learning algorithm based on a hybrid thresholding followed with a Gaussian mixture model (GMM) is presented in this paper. The EEG signal is priliminarily screened by a cascaded thresholding method built on the distributions of signal amplitude, amplitude displacement, as well as the cross channel correlation. Then, the channel correlation of the two frontal electrodes (FP1, FP2), the fractal dimension, and the mean of amplitude difference between FP1 and FP2, are extracted to characterize the filtered EEGs. The GMM trained on these features is applied for the eye blink detection. The performance of the proposed algorithm is studied on two EEG datasets collected by the Temple University Hospital (TUH) and the Children's Hospital, Zhejiang University School of Medicine (CHZU), where the datasets are recorded from epilepsy and encephalitis patients, and contain a lot of eye blink artifacts. Experimental results show that the proposed algorithm can achieve the highest detection precision and F1 score over the state-of-the-art methods.

An Adaptive Kalman Filter Bank for ECG Denoising

Abstract: Model-based Bayesian frameworks proved their effectiveness in the field of ECG processing. However, their performances rely heavily on the pre-defined models extracted from ECG signals. Furthermore, their performances decrease substantially when ECG signals do not comply with their models- a situation generally occurs in the case of arrhythmia-. In this paper, we propose a novel Bayesian framework based on Kalman filter, which does not need a predefined model and can adapt itself to different ECG morphologies. Compared with the previous Bayesian techniques, the proposed method requires much less preprocessing and it only needs to know the location of R-peaks to start ECG processing. Our method uses a filter bank comprised of two adaptive Kalman filters, one for denoising QRS complex (high frequency section) and another one for denoising P and T waves (low frequency section). The parameters of these filters are estimated and iteratively updated using expectation maximization (EM) algorithm. In order to deal with nonstationary noises such as muscle artifact (MA) noise, we used Bryson and Henrikson's technique for the prediction and update steps inside the Kalman filter bank. We evaluated the performance of the proposed method on different ECG databases containing signals having morphological changes and abnormalities such as atrial premature complex (APC), premature ventricular contractions (PVC), Ventricular Tachyarrhythmia (VT) and sudden cardiac death (SCD). The proposed algorithm was compared with several popular ECG denoising methods such as wavelet transform (WD) and empirical mode decomposition (EMD). The comparison results showed that the proposed method performs well in the presence of various ECG morphologies in both stationary and non-stationary environments especially at low input SNRs.

VME-DWT: An Efficient Algorithm for Detection and Elimination of Eye Blink From Short Segments of Single EEG Channel

Abstract: Objective: Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications in health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however, are often contaminated by eye blink artifacts that can yield the fallacious interpretation of the brain activity. This paper proposes an efficient algorithm, VME-DWT, to remove eye blinks in a short segment of the single EEG channel. Method: The proposed algorithm: (a) locates eye blink intervals using Variational Mode Extraction (VME) and (b) filters only contaminated EEG interval using an automatic Discrete Wavelet Transform (DWT) algorithm. The performance of VME-DWT is compared with an automatic Variational Mode Decomposition (AVMD) and a DWT-based algorithms, proposed for suppressing eye blinks in a short segment of the single EEG channel. Results: The VME-DWT detects and filters 95% of the eye blinks from the contaminated EEG signals with SNR ranging from −8 to +3 dB. The VME-DWT shows superiority to the AVMD and DWT with the higher mean value of correlation coefficient (0.92 vs. 0.83, 0.58) and lower mean value of RRMSE (0.42 vs. 0.59, 0.87). Significance: The VME-DWT can be a suitable algorithm for removal of eye blinks in low-cost single-channel EEG systems as it is: (a) computationally-efficient, the contaminated EEG signal is filtered in millisecond time resolution, (b) automatic, no human intervention is required, (c) low-invasive, EEG intervals without contamination remained unaltered, and (d) low-complexity, without need to the artifact reference.

Training a neural network for Gibbs and noise removal in diffusion MRI

Abstract: Purpose To develop and evaluate a neural network-based method for Gibbs artifact and noise removal. Methods A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. Results Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. Conclusions The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.

Wavelet Domain Optimized Savitzky–Golay Filter for the Removal of Motion Artifacts From EEG Recordings

Abstract: Motion artifact is observed in electroencephalogram (EEG) signals during the acquisition. The elimination of this type of artifact using various signal processing approaches is considered a preprocessing task for different neural information processing applications. In this article, the wavelet domain optimized Savitzky–Golay (WOSG) filtering approach was proposed for the removal of motion artifacts from EEG signals. The multiscale analysis of the EEG signals using discrete wavelet transform (DWT) produces subband signals at different scales. Motion artifact is a low-frequency artifact that appears in the approximation subband signal. The optimized SG filter was applied to the motion artifact intermixed approximation subband signal, and the cleaned approximation subband signal was evaluated based on the subtraction of the optimized SG filter output from the motion artifact intermixed subband signal. The filtered EEG signal was computed based on the addition of cleaned approximation subband signal with other subband signals of contaminated EEG. The proposed WOSG filtering approach was evaluated using EEG recordings from various publicly available databases. Measures, such as the mean absolute error in power spectral density (MAE-PSD) of $\delta $ -band between contaminated and cleaned EEG signal, $\Delta $ SNR, percentage change in correlation coefficients ( $\eta $ ), and mutual information (MI), were used to quantify the performance of the proposed filtering approach. The results revealed that the proposed WOSG filtering approach had superior denoising performance with the average $\Delta $ SNR, $\eta $ , and MAE-PSD values of 30.59 dB, 68.76%, and 0.0263 dB/Hz in comparison to the multiresolution total variation (MTV) ( $\Delta $ SNR as 29.12 dB, $\eta $ as 68.56%, and MAE-PSD as 0.0365 dB/Hz) and other existing methods. The approach had the average MI values of 4.152 and 4.103 and the average MAE-PSD values of 0.276 and 0.256 dB/Hz for $\delta $ -bands of EEG signals recorded during standing and walking conditions.

Does Earthquake Stress Drop Increase with Depth in the Crust

Abstract: We combine earthquake spectra from multiple studies to investigate whether the increase in stress drop with depth often observed in the crust is real, or an artifact of decreasing attenuation (incr...

Noise Reduction in ECG Signal Using Combined Ensemble Empirical Mode Decomposition Method with Stationary Wavelet Transform

Abstract: The diagnostic study of electrocardiography (ECG) signals plays a vital role in the diagnosis of cardiac problems. But the powerline interference in ECG causes an artifact in the interpretation of the original signal. In this paper, a new method for the removal of such noise/artifact from the ECG signal by combining stationary wavelet transform with empirical mode decomposition (EMD-SWT) and ensemble empirical mode decomposition (EEMD-SWT) is proposed. SWT is applied after the decomposition of ECG signals into various intrinsic mode functions (IMFs) for further removal of noise. The proposed methods are tested for various datasets available in MIT-BIH Arrhythmia database, and the performance has been validated with existing methods. From the simulated results, it is found that combining SWT with EMD and EEMD yields better SNR enhancement when compared to the traditional methods.

Comment on "Boosted molecular mobility during common chemical reactions".

Abstract: The apparent "boosted mobility" observed by Wang et al (Reports, 31 July 2020, p. 537) is the result of a known artifact. When signal intensities are changing during a nuclear magnetic resonance (NMR) diffusion measurement for reasons other than diffusion, the use of monotonically increasing gradient amplitudes produces erroneous diffusion coefficients. We show that no boosted molecular mobility is observed when shuffled gradient amplitudes are applied.

ECG signal analysis using CWT, spectrogram and autoregressive technique

Abstract: The cardiovascular system is a combination of the heart, blood and blood vessels. Cardiovascular diseases (CVD) are a key factor behind casualties worldwide among both women and men. About 9.4 million deaths occur due to high Blood Pressure (BP) only, out of which 51% deaths are due to strokes and 45% deaths are due to coronary heart diseases. The Electrocardiogram (ECG) represents the heart health condition of the subject, (patient) since it is acquired through electrical conduction, which appears in terms of P-QRS-T waves. But analysis of these waves is very tedious due to the existence of different noises/artifacts. Computer Aided Diagnosis (CAD) system is required in practical medical scenario for better and automated ECG signal analysis and to compensate for human errors. In general, implementation of a CAD system for ECG signal analysis requires; preprocessing, feature extraction and classification. In the existing literature, some authors have used time domain techniques which yield good performance for cleaned ECG signals i.e., without noise/artifact. Some authors have used frequency domain techniques later, but they suffer from the problem of spectral leakage making them unsuitable for real time/pathological datasets. The existing techniques from both these domains are not able to effectively analyze nonlinear behavior of ECG signals. These limitations have motivated this work where Continuous Wavelet Transform (CWT), Spectrogram and Autoregressive (AR) technique are used collectively for interpreting nonlinear and non-stationary features of the ECG signals. In this paper, both Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia database (MB Ar DB) and Real-time database (RT DB) have been used. Performance of the proposed method is compared with that of the previous studies on the basis of sensitivity (SE) and detection rate (D.R). The proposed technique yields SE of 99.90%, D.R of 99.81% & SE of 99.77%, D.R of 99.87% for MB Ar DB and RT DB, respectively. Therefore, the proposed technique showcases the possibility of an encouraging diagnostic tool for further improving the present situation of health informatics in cardiology labs/hospitals.

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

Abstract: 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.

Variational Formulation of Unsupervised Deep Learning for Ultrasound Image Artifact Removal

Abstract: Recently, deep learning approaches have been successfully used for ultrasound (US) image artifact removal. However, paired high-quality images for supervised training are difficult to obtain in many practical situations. Inspired by the recent theory of unsupervised learning using optimal transport driven CycleGAN (OT-CycleGAN), here, we investigate the applicability of unsupervised deep learning for US artifact removal problems without matched reference data. Two types of OT-CycleGAN approaches are employed: one with the partial knowledge of the image degradation physics and the other with the lack of such knowledge. Various US artifact removal problems are then addressed using the two types of OT-CycleGAN. Experimental results for various unsupervised US artifact removal tasks confirmed that our unsupervised learning method delivers results comparable to supervised learning in many practical applications.

Eye-blink artifact removal from single channel EEG with k-means and SSA.

Abstract: In recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ( $$\Gamma $$ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

EEGANet: Removal of Ocular Artifact from the EEG Signal Using Generative Adversarial Networks.

Abstract: The elimination of ocular artifacts is critical in analyzing electroencephalography (EEG) data for various brain-computer interface (BCI) applications. Despite numerous promising solutions, electrooculography (EOG) recording or an eye-blink detection algorithm is required for the majority of artifact removal algorithms. This reliance can hinder the model's implementation in real-world applications. This paper proposes EEGANet, a framework based on generative adversarial networks (GANs), to address this issue as a data-driven assistive tool for ocular artifacts removal (source code is available at https://github.com/IoBT-VISTEC/EEGANet). \textcolor{red}{After the model was trained, the removal of ocular artifacts could be applied calibration-free without relying on the EOG channels or the eye blink detection algorithms.} First, we tested EEGANet's ability to generate multi-channel EEG signals, artifacts removal performance, and robustness using the EEG eye artifact dataset, which contains a significant degree of data fluctuation. According to the results, EEGANet is comparable to state-of-the-art approaches that utilize EOG channels for artifact removal. Moreover, we demonstrated the effectiveness of EEGANet in BCI applications utilizing two distinct datasets under inter-day and subject-independent schemes. Despite the absence of EOG signals, the classification performance of the signals processed by EEGANet is equivalent to that of traditional baseline methods. This study demonstrates the potential for further use of GANs as a data-driven artifact removal technique for any multivariate time-series bio-signal, which might be a valuable step towards building next-generation healthcare technology.

Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation

Abstract: Recently, deep learning approaches for MR motion artifact correction have been extensively studied. Although these approaches have shown high performance and lower computational complexity compared to classical methods, most of them require supervised training using paired artifact-free and artifact-corrupted images, which may prohibit its use in many important clinical applications. For example, transient severe motion (TSM) due to acute transient dyspnea in Gd-EOB-DTPA-enhanced MR is difficult to control and model for paired data generation. To address this issue, here we propose a novel unpaired deep learning scheme that does not require matched motion-free and motion artifact images. Specifically, the first step of our method is ${k}$ -space random subsampling along the phase encoding direction that can remove some outliers probabilistically. In the second step, the neural network reconstructs fully sampled resolution image from a downsampled ${k}$ -space data, and motion artifacts can be reduced in this step. Last, the aggregation step through averaging can further improve the results from the reconstruction network. We verify that our method can be applied for artifact correction from simulated motion as well as real motion from TSM successfully from both single and multi-coil data with and without ${k}$ -space raw data, outperforming existing state-of-the-art deep learning methods.

Paving the Way for Future EEG Studies in Construction: Dependent Component Analysis for Automatic Ocular Artifact Removal from Brainwave Signals

Abstract: Construction workers’ poor mental states can lead to numerous safety and productivity issues. One major trend in construction research is quantitatively evaluating workers’ psychophysiologi...

A Bidirectional Neural Interface SoC With Adaptive IIR Stimulation Artifact Cancelers

Abstract: We present a 180-nm CMOS bidirectional neural interface system-on-chip that enables simultaneous recording and stimulation with on-chip stimulus artifact cancelers The front-end (FE) cancellation scheme incorporates a least-mean-squares (LMS) engine that adapts the coefficients of a two-tap infinite-impulse-response filter to replicate the stimulation artifact waveform and subtract it at the FE Measurements demonstrate the efficacy of the canceler in mitigating artifacts up to 700 mVpp and reducing the FE amplifier saturation recovery time in response to a 25-Vpp artifact Each recording channel houses a pair of adaptive infinite-impulse-response filters, which enables the cancellation of the artifacts generated by the simultaneous operation of the two on-chip stimulators The analog FE consumes $25~\mu \text{W}$ of power per channel and has a maximum gain of 50 dB and a bandwidth of 90 kHz with 62- $\mu \text{V}_{\text {rms}}$ integrated input-referred noise

On the Mutual Interference Between Spaceborne SARs: Modeling, Characterization, and Mitigation

Abstract: As the radio spectrum available to spaceborne synthetic aperture radar (SAR) is restricted to certain limited frequency intervals, there are many different spaceborne SAR systems sharing common frequency bands. Due to this reason, it is reported that two spaceborne SARs at orbit cross positions can potentially cause severe mutual interference. Specifically, the transmitting signal of an SAR, typically linear frequency modulated (LFM), can be directly received by the side or back lobes of another SAR’s antenna, causing radiometric artifacts in the focused image. This article tries to model and characterize the artifacts and study efficient methods for mitigating them. To this end, we formulate an analytical model for describing the artifact, which reveals that the mutual interference can introduce a 2-D LFM radiometric artifact in image domain with a limited spatial extent. We show that the artifact is low-rank based on a range–azimuth decoupling analysis and 2-D high-order Taylor expansion. Based on the low-rank model, we show that two methods, i.e., principal component analysis and its robust variant, can be adopted to efficiently mitigate the artifact via processing in the image domain. The former method has the advantage of fast processing speed, for example, a subswath of Sentinel-1 interferometric wide swath image can be processed within 70 s via blockwise processing, whereas the latter provides improved accuracy for sparse pointlike scatterers. Experiment results demonstrate that the radiometric artifacts caused by mutual interference in Sentinel-1 level-1 images can be efficiently mitigated via the proposed methods.