An Effective and Robust Framework for Ocular Artifact Removal From Single-Channel EEG Signal Based on Variational Mode Decomposition
TL;DR: This paper proposes a robust framework for the detection and removal of OAs based on variational mode decomposition (VMD) and turning point count and demonstrates that this framework outperforms few existing OAs removal techniques in removing OAs from single-channel EEG signal.
Abstract: Removal of ocular artifacts (OAs) from electroencephalogram (EEG) signal is crucial for accurate and effective EEG analysis and brain-computer interface research. The elimination of OAs is quite challenging in absence of reference electro-oculogram and in single-channel EEG signal using existing independent component analysis based OA removal techniques. Though few of the recent OAs removal techniques suppress the OAs in the single-channel significantly, these techniques introduce distortion in clinical features of the EEG signal during artifact removal process. To address these issues, in this paper, we propose a robust framework for the detection and removal of OAs based on variational mode decomposition (VMD) and turning point count. The proposed framework exploits the effectiveness of VMD in two stages denoted as VMD-I and VMD-II respectively. The proposed framework has four components: EEG signal decomposition into two modes using VMD-I; rejection of low-frequency baseline components; processed EEG signal decomposition into three modes using VMD-II; rejection of mode containing OAs based on turning point count based threshold criteria. We evaluate the effectiveness of the proposed framework using the EEG signals in presence of various ocular artifacts with different amplitudes and shapes taken from three standard databases including, Mendeley database, MIT-BIH Polysmnographic database and EEG during mental arithmetic tasks database. Evaluation results demonstrate that proposed framework eliminates OAs with minimal loss in valuable clinical features in both reconstructed EEG signal and in all local rhythms. Furthermore, subjective and objective comparative analysis demonstrate that our framework outperforms few existing OAs removal techniques in removing OAs from single-channel EEG signal.
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26 Jan 2021
TL;DR: In this article, the authors proposed an efficient algorithm, VME-DWT, to remove eye blinks in a short segment of the single EEG channel, which locates eye blink intervals using Variational Mode Extraction (VME) and filters only contaminated EEG interval using an automatic Discrete Wavelet Transform (DWT) algorithm.
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.
34 citations
TL;DR: This paper attempts to give an extensive outline of the advancement in methodologies to eliminate one of the most common artifacts, i.e., ocular artifact, from EEG signal with a validated simulation model on the recorded EEG signal.
24 citations
TL;DR: Two robust distortion measures such as weighted signal to noise ratio (WSNR) and weighted correlation coefficient (WCC) for accurately representing the objective reconstruction loss in each band of EEG signal are proposed.
Abstract: An objective distortion measure is very crucial to accurately quantify the distortion introduced in the electroencephalogram (EEG) signal during the denoising process Most of the existing algorithms report their denoising performance by comparing the original EEG signal and the reconstructed EEG signal using root mean square error (RMSE) and other similar measures However, it is very important to quantify the distortion in each band of EEG signal since each band provides distinct information about the specific brain activity Furthermore, quantification of band-wise distortion enables the selection of particular denoising algorithm for the application-specific EEG signal analysis Therefore, in this paper, we propose two robust distortion measures such as weighted signal to noise ratio (WSNR) and weighted correlation coefficient (WCC) for accurately representing the objective reconstruction loss in each band These performance measures are computed between the wavelet subbands of the original and the denoised/reconstructed signal with weights equal to the relative wavelet energy and wavelet entropy of the corresponding subband wavelet coefficients To demonstrate the effectiveness of the proposed performance measures, we evaluate the performance of six existing denoising methods using these measures Results depict that these measures can adequately provide high mutual agreement between objective scores and subjective analysis
18 citations
TL;DR: A hybrid system to automatically remove eye-blink artifacts from the EEG by combining several methods, such as Independent Component Analysis (ICA), Kurtosis, K-means, Modified Z-Score (MZS) and Adaptive Noise Canceller (ANC), is introduced.
11 citations
TL;DR: In this article , a shallow one-dimensional convolutional neural network (1D-CNN) architecture was proposed for cognitive task classification using single/limited channel electroencephalogram (EEG) signals in real-time.
11 citations
References
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TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Abstract: —The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of He...
11,407 citations
TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
Abstract: During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical attempts to this decomposition problem, like synchrosqueezing, empirical wavelets or recursive variational decomposition. Here, we propose an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently. The model looks for an ensemble of modes and their respective center frequencies, such that the modes collectively reproduce the input signal, while each being smooth after demodulation into baseband. In Fourier domain, this corresponds to a narrow-band prior. We show important relations to Wiener filter denoising. Indeed, the proposed method is a generalization of the classic Wiener filter into multiple, adaptive bands. Our model provides a solution to the decomposition problem that is theoretically well founded and still easy to understand. The variational model is efficiently optimized using an alternating direction method of multipliers approach. Preliminary results show attractive performance with respect to existing mode decomposition models. In particular, our proposed model is much more robust to sampling and noise. Finally, we show promising practical decomposition results on a series of artificial and real data.
4,111 citations
"An Effective and Robust Framework f..." refers background or methods in this paper
...VMD is a recent non-recursive signal decomposition technique which can derive underlying structure of a non-stationary signal [28]–[30]....
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...The iterative process involved in VMD can be summarized as [28]....
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...Let a band-limited component be b(t) = e(t)cos(φ(t)), where, φ(t) represents a non-decreasing phase parameter, e(t) denotes the envelope of the component and ω(t) = dφ(t) dt is the instantaneous frequency [28]–[30]....
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...term and Lagrangian multiplier) can be studied from [28]....
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TL;DR: This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts, and concludes that the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second- order blind identification (SOBI).
Abstract: This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.
640 citations
"An Effective and Robust Framework f..." refers background in this paper
...ELECTROENCEPHALOGRAM (EEG) is a non-invasive approach to measure neuronal electrical activity to analyze the normal and abnormal brain activities [1]....
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...motor rehabilitation, measurement of mental health conditions, neuroscience, psycho-physiological research, and cognitive training [1], [2]....
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TL;DR: Although the focus is on eliminating ocular artifacts in EEG data, the approach can be extended to other sources of EEG contamination such as cardiac signals, environmental noise, and electrode drift, and adapted for use with magnetoencephalographic (MEG) data, a magnetic correlate of EEG.
Abstract: Signals from eye movements and blinks can be orders of magnitude larger than brain-generated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data. Rejecting contaminated trials causes substantial data loss, and restricting eye movements/blinks limits the experimental designs possible and may impact the cognitive processes under investigation. This article presents a method based on blind source separation (BSS) for automatic removal of electroocular artifacts from EEG data. BBS is a signal-processing methodology that includes independent component analysis (ICA). In contrast to previously explored ICA-based methods for artifact removal, this method is automated. Moreover, the BSS algorithm described herein can isolate correlated electroocular components with a high degree of accuracy. Although the focus is on eliminating ocular artifacts in EEG data, the approach can be extended to other sources of EEG contamination such as cardiac signals, environmental noise, and electrode drift, and adapted for use with magnetoencephalographic (MEG) data, a magnetic correlate of EEG.
608 citations
"An Effective and Robust Framework f..." refers methods in this paper
...In [15], a hierarchical clustering based automatic OAs removal method is proposed using features such as kurtosis, median, average band power of EEG waves....
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TL;DR: This work shows that a "leak" of cerebral activity of interest into components marked as artificial means that one is going to lost that activity, and proposes a novel wavelet enhanced ICA method (wICA) that applies a wavelet thresholding not to the observed raw EEG but to the demixed independent components as an intermediate step.
472 citations
"An Effective and Robust Framework f..." refers background in this paper
...in presence of dominant neuronal activity of δ and θ since the electrical brain activity in these rhythms dominates in case of deep sleep and moderate sleep [12]....
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