Wavelet Based Waveform Distortion Measures for Assessment of Denoised EEG Quality With Reference to Noise-Free EEG Signal
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
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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: EEG-GCN is proposed, a paradigm that adopts spatio-temporal and self-adaptive graph convolutional networks for single and multi-view EEG-based emotion recognition and outperforms other representative methods from both single and multiple views.
Abstract: Graph networks are naturally suitable for modeling multi-channel features of EEG signals. However, the existing study that attempts to utilize graph-based neural networks for EEG-based emotion recognition doesn’t take the spatio-temporal redundancy of EEG features and differences in brain topology into account. In this paper, we propose EEG-GCN, a paradigm that adopts spatio-temporal and self-adaptive graph convolutional networks for single and multi-view EEG-based emotion recognition. With spatio-temporal attention mechanism employed, EEG-GCN can adaptively capture significant sequential segments and spatial location information in EEG signals. Meanwhile, a self-adaptive brain network adjacency matrix is designed to quantify the connection strength between the channels, in which way to represent the diverse activation patterns under different emotion scenarios. Additionally, we propose a multi-view EEG-based emotion recognition method, which effectively integrates the diverse features of EEG signals. Extensive experiments conducted on two benchmark datasets SEED and DEAP demonstrate that our proposed method outperforms other representative methods from both single and multiple views.
8 citations
TL;DR: In this paper , an autonomous robot alertness mechanism framework was proposed by using the deep reinforcement learning model of the human alertness mechanisms, and a fast K-T filtering algorithm was developed to eliminate the multiple noises of the electroencephalograph (EEG) signals by the blind source separation and the adjustable factor wavelet transform.
Abstract: Alertness mechanism of unmanned monitoring vehicles to environment is important. Especially, the vigilance modeling of underground security robots has a particularly significance because the underground is a dangerous environment. However, there is no a mature methodology for the alertness computation. In this work, four parts of the alertness estimation are focused. First, an autonomous robot alertness mechanism framework is proposed by using the deep reinforcement learning model of the human alertness mechanism. Second, a fast K-T filtering algorithm is developed to eliminate the multiple noises of the electroencephalograph (EEG) signals by the blind source separation and the adjustable $Q$ factor wavelet transform. Third, the description problem of the directed interaction stability of the cortical EEG signals is solved by the ensemble empirical mode decomposition and the directional transfer function. Fourth, the human alertness estimation is explored by using the support vector regression of the dynamically spatial-temporal brain network connection parameters. Experiments show that, the mean square error and the determination coefficient of the explored alertness estimation are respectively 0.115 and 0.8337. Compared with the scalp EEG alertness estimation, it has a better performance because the mean square error is decreased by 0.0684, and the determination coefficient is increased by 0.023.
5 citations
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TL;DR: In this paper , the authors proposed an enhanced procedure for the optimal individuation of ASR parameters, in order to successfully remove artifacts in low-density EEG acquisitions (down to four channels).
Abstract: Electroencephalogram (EEG) plays a significant role in the analysis of cerebral activity, although the recorded electrical brain signals are always contaminated with artifacts. This represents the major issue limiting the use of the EEG in daily life applications, as the artifact removal process still remains a challenging task. Among the available methodologies, artifact subspace reconstruction (ASR) is a promising tool that can effectively remove transient or large-amplitude artifacts. However, the effectiveness of ASR and the optimal choice of its parameters have been validated only for high-density EEG acquisitions. In this regard, this study proposes an enhanced procedure for the optimal individuation of ASR parameters, in order to successfully remove artifacts in low-density EEG acquisitions (down to four channels). The proposed method starts from the analysis of real EEG data, to generate a large semisimulated dataset with similar characteristics. Through a fine-tuning procedure on this semisimulated data, the proposed method identifies the optimal parameters to be used for artifact removal on real data. The results show that the algorithm achieves an efficient removal of artifacts preserving brain signal information, also in low-density EEG signals, thus favoring the adoption of the EEG also for more portable and/or daily-life applications.
3 citations
TL;DR: In this article , a hybrid signal denoising methodology which includes empirical wavelet transforms (EWT), adaptive threshold-based nonlinear Teager-Kaiser energy operator (TEO), and customized morphological filter in accompanying with modified ensemble average subtraction (MEAS) is proposed for automatic detection and suppression of cardiac artifact from a single-channel EEG.
Abstract: Sleep is one of the prime natural activities for human well-being in physical, emotional, and mental aspects. The assessment of sleep Electroencephalography (EEG) signals is required to diagnose sleep-related neurological disorders. It is found that sleep EEG signals are extremely vulnerable to highly energetic electrocardiogram (ECG) signals. The intermixing of ECG into EEG, commonly known as cardiac artifacts, might severely affect the sleep EEG data. In order to have artifact-free EEG signal, a hybrid signal denoising methodology which includes empirical wavelet transforms (EWT), adaptive threshold-based nonlinear Teager-Kaiser energy operator (TEO), and customized morphological filter in accompanying with modified ensemble average subtraction (MEAS) is proposed for automatic detection and suppression of cardiac artifact from a single-channel EEG. The efficacy of the proposed methodology presented in the paper has been evaluated over standard public datasets such as CinC Challenge 2014 dataset (synthetic), and MIT-BIH polysomnography data (clinical). It has been observed that the proposed method outperforms other state-of-the-art automated EEG artifact elimination methods in terms of few popular denoising performance indexes such as signal to artifact ratio, percentage root mean square difference, percentage distortion in power spectral density, structural similarity index measure, and execution time. The proposed method is robust, time-efficient, and preserves the majority of EEG data with minimal loss, making it suitable for neuro clinical EEG analysis.
2 citations
References
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TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Abstract: Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. >
20,028 citations
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: The results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods.
Abstract: Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.
2,944 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
TL;DR: The major objective of the present work was to characterize in a quantitative way functional dynamics of order/disorder microstates in short duration EEG signals with specific quantifiers derived to characterize how stimulus affects electrical events in terms of frequency synchronization (tuning) in the event related potentials.
780 citations