Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal.
14 Apr 2020-Healthcare technology letters (Institution of Engineering and Technology (IET))-Vol. 7, Iss: 2, pp 35-40
TL;DR: The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT).
Abstract: This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single-channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the use of reference electromyogram (EMG). The proposed method involves three major steps: decomposition of the input EEG signal into two modes using VMD; detection of MAs based on zero crossings count thresholding in the second mode; retention of the first mode as MAs-free EEG signal only after detection of MAs in the second mode. The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT). Evaluation results using different objective performance metrics depict the superiority of the proposed method as compared to existing methods while preserving the clinical features of the reconstructed EEG signal.
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TL;DR: In this article, the authors compared both classical and advanced approaches for noise contamination removal such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.
Abstract: As it was mentioned in the previous part of this work (Part I)—the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work—various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.
15 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
TL;DR: In this article, the authors proposed a state-dependent joint blind source separation (JBSS) model by integrating the hidden Markov model with independent vector analysis in a maximum likelihood framework to identify the varying sources of muscle artifact components and underlying EEG signals.
Abstract: Electroencephalography (EEG) is an important noninvasive neural recording technique with a broad application in the field of neurological instrumentation and measurement. However, EEG signals are often contaminated by muscle artifacts, adversely affecting the subsequent analysis. Joint blind source separation (JBSS) models have been successfully applied to remove muscle artifacts from EEG recordings, although most of them were designed for EEG collected in well-controlled conditions. Without considering the dynamics of underlying mixtures in complex environments may hinder the real mobile and long-term healthcare monitoring. To deal with such concern, we assume that the mixing process of sources dynamically changes over time and propose a state-dependent JBSS model by integrating the hidden Markov model with independent vector analysis in a maximum likelihood framework. It is capable of identifying the varying sources of muscle artifact components and underlying EEG signals. The proposed method was evaluated on both simulated and semi-simulated data, and demonstrated superior performance compared with other popular approaches for muscle artifacts removal in dynamic environments. The state-dependent JBSS model provides a novel way to investigate the temporal dynamics of multiple multidimensional biomedical data sets simultaneously.
9 citations
TL;DR: In this article, the authors make an attempt to study the usage of optimization techniques in formulating the issues in brain-computer interfaces (BCI), and the outcomes, challenges, and major observations of these approaches are discussed in detail.
Abstract: Electroencephalogram (EEG) is one of the common modalities of monitoring the mental activities. Owing to the non-invasive availability of this system, its applicability has seen remarkable developments beyond medical use-cases. One such use case is brain-computer interfaces (BCI). Such systems require the usage of high resolution-based multi-channel EEG devices so that the data collection spans multiple locations of the brain like the occipital, frontal, temporal, and so on. This results in huge data (with high sampling rates) and with multiple EEG channels with inherent artifacts. Several challenges exist in analyzing data of this nature, for instance, selecting the optimal number of EEG channels or deciding what best features to rely on for achieving better performance. The selection of these variables is complicated and requires a lot of domain knowledge and non-invasive EEG monitoring, which is not feasible always. Hence, optimization serves to be an easy to access tool in deriving such parameters. Considerable efforts in formulating these issues as an optimization problem have been laid. As a result, various multi-objective and constrained optimization functions have been developed in BCI that has achieved reliable outcomes in device control like neuro-prosthetic arms, application control, gaming, and so on. This paper makes an attempt to study the usage of optimization techniques in formulating the issues in BCI. The outcomes, challenges, and major observations of these approaches are discussed in detail.
9 citations
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TL;DR: This paper proposes a light-weight one-dimensional convolutional neural network (1D-CNN) architecture for mental task identification and classification which outperforms existing approaches not only in terms of classification accuracy but also in robustness against artifacts.
Abstract: Mental task identification and classification using single/limited channel(s) electroencephalogram (EEG) signals in real-time play an important role in the design of portable brain-computer interface (BCI) and neurofeedback (NFB) systems. However, the real-time recorded EEG signals are often contaminated with noises such as ocular artifacts (OAs) and muscle artifacts (MAs), which deteriorate the hand-crafted features extracted from EEG signal, resulting inadequate identification and classification of mental tasks. Therefore, we investigate the use of recent deep learning techniques which do not require any manual feature extraction or artifact suppression step. In this paper, we propose a light-weight one-dimensional convolutional neural network (1D-CNN) architecture for mental task identification and classification. The robustness of the proposed architecture is evaluated using artifact-free and artifact-contaminated EEG signals taken from two publicly available databases (i.e, Keirn and Aunon ($K$) database and EEGMAT ($E$) database) and in-house ($R$) database recorded using single-channel neurosky mindwave mobile 2 (MWM2) EEG headset in performing not only mental/non-mental binary task classification but also different mental/mental multi-tasks classification. Evaluation results demonstrate that the proposed architecture achieves the highest subject-independent classification accuracy of $99.7\%$ and $100\%$ for multi-class classification and pair-wise mental tasks classification respectively in database $K$. Further, the proposed architecture achieves subject-independent classification accuracy of $99\%$ and $98\%$ in database $E$ and the recorded database $R$ respectively. Comparative performance analysis demonstrates that the proposed architecture outperforms existing approaches not only in terms of classification accuracy but also in robustness against artifacts.
8 citations
Cites background or methods from "Effective automated method for dete..."
...ed channel(s) EEGs are commonly corrupted with various ocular and muscle artifacts, performance of the hand-crafted features-based mental task identification techniques deteriorates significantly [17], [18]. Recently, deep convolutional neural network (CNN) has gained attention due to its ability to extract high level features automatically from the raw data for accurate analysis of different physiologi...
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...racy for both artifact-free and artifact-contaminated EEG signals. Furthermore, use of artifact removal step can alter clinical features of EEG signals even in case of artifact-free EEG signals [17], [18]. Existing CNN-based mental task and mental workload classification techniques use complex architecture and input signal in the form of 2D or 3D time-frequency representations of single/multi-channel E...
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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
TL;DR: Dynamical properties of brain electrical activity from different recording regions and from different physiological and pathological brain states are compared and strongest indications of nonlinear deterministic dynamics were found for seizure activity.
Abstract: We compare dynamical properties of brain electrical activity from different recording regions and from different physiological and pathological brain states Using the nonlinear prediction error and an estimate of an effective correlation dimension in combination with the method of iterative amplitude adjusted surrogate data, we analyze sets of electroencephalographic (EEG) time series: surface EEG recordings from healthy volunteers with eyes closed and eyes open, and intracranial EEG recordings from epilepsy patients during the seizure free interval from within and from outside the seizure generating area as well as intracranial EEG recordings of epileptic seizures As a preanalysis step an inclusion criterion of weak stationarity was applied Surface EEG recordings with eyes open were compatible with the surrogates' null hypothesis of a Gaussian linear stochastic process Strongest indications of nonlinear deterministic dynamics were found for seizure activity Results of the other sets were found to be inbetween these two extremes
2,387 citations
"Effective automated method for dete..." refers methods in this paper
...Test databases and performance metrics: The proposed method has been tested on EEG and EMG signals taken from five publicly available databases including Mendeley database [25], epileptic Bonn database (set ‘Z’) [26], EEG during mental arithmetic tasks (EEGMAT) [27], examples of electromyograms [27] and cerebral vasoregulation in elderly with stroke (CVES) database [27]....
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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
TL;DR: Frontalis or temporalis muscle EMG recorded from the scalp has spectral and topographical features that vary substantially across individuals, and EMG spectra often have peaks in the beta frequency range that resemble EEG beta peaks.
600 citations
"Effective automated method for dete..." refers background or methods in this paper
...Since digital filtering based methods use low-pass filters in order to suppress the high-frequency content from the MA-contaminated EEG data, EEG content is also distorted in the filtering process due to spectral overlap between EEG and MAs [4, 5]....
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...However, these artefacts are difficult to standardise as the spectral and topographical characteristics of MAs vary based on the magnitude and particular type of muscle contraction, and different individuals [3, 5]....
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