Subband Signals Research Articles

Joint Singular Spectrum Analysis and Generalized Moreau Envelope Total Variation for motion artifact removal from single channel EEG signals

Recorded electroencephalogram (EEG) signals are commonly interfered by several artifacts. It is very crucial to remove the artifacts for automatic detection of brain disorders. In this work, the joint use of Singular Spectrum Analysis (SSA) and Generalized Moreau Envelope Total Variation (GMETV), namely SSA-GMETV technique is proposed to remove the motion artifacts from the single channel EEG signals. In this work, initially, the interfered EEG is decomposed into several bands by SSA. The last subband of the interfered signal is applied to GMETV filter to extract the artifact. This is then subtracted from the last subband signal and added to the remaining SSA decomposed bands, to obtain the denoised EEG signal. Relative Root Mean Square Error (RRMSE) and difference in Signal to Noise Ratio (ΔSNR) are considered to quantify the denoised EEG signals. Experimental results demonstrate that the proposed SSA-GMETV technique performs much better than the existing methods in terms of the performance metrics, average RRMSE and average ΔSNR by 47.41% and 31.81 dB respectively.

A new, enhanced EZW image codec with subband classification

In this paper, an enhanced version of Embedded zerotree wavelet (EZW) image coding algorithm is proposed, referred to as EZW-SC. By exploiting a new principle that relies on a subband classification concept, the enhanced algorithm allows the prediction of insignificant subbands at early passes, along with the use of an improved significance map. This reduces the redundancy of zerotree symbols, speeds up the coding process and improves the coding of significant coefficients. In fact, the EZW-SC algorithm scans only significant subbands and significantly improves the lossy compression performance with the conventional EZW. Moreover, new EZW-based schemes are presented to perform colour image coding by taking advantage of the interdependency of the colour components. Experimental results show clear superiority of the proposed algorithms over the conventional EZW as well as other related EZW schemes at various bit rates in both greyscale and colour image compression.

Adaptive Subband Compression for Streaming of Continuous Point-on-Wave and PMU Data

A data compression system capable of providing real-time streaming of high-resolution continuous point-on-wave (CPOW) and phasor measurement unit (PMU) measurements is proposed. Referred to as adaptive subband compression (ASBC), the proposed technique partitions the signal space into subbands and adaptively compresses subband signals based on each subband's active bandwidth. The proposed technique conforms to existing industry phasor measurement standards, making it suitable for streaming high-resolution CPOW and PMU data either in continuous or burst on-demand/event-triggered modes. Experiments on synthetic and real data show that ASBC reduces the CPOW sampling rates by several orders of magnitude for real-time streaming while maintaining the precision required by industry standards.

High resolution sub-band decomposition underdetermined blind signal separation using virtual sensor based ICA method for low latency applications

Underdetermined blind source separation (UBSS) objective is to recover the source signals from a number of mixtures without any information about the mixing system. Technologies such as teleconferencing, hearing aids and hands-free telephony require real time processing, as long delays are considered intolerable in interactive two-way communication. This is a major challenge in BSS, as algorithms generally require significant amounts of data (several seconds of data or more) to generate sufficient statistics for separation. In this paper, we propose a hybrid algorithm which combines sub-band decomposition and well-known Independent Component Analysis (ICA) based algorithm, Extended-Infomax. We first employ sub-band decomposition algorithm in sparse time domain to compensate low data efficiency of short time block lengths and estimate the mixing matrix. Subsequently, the proposed virtual sensor based underdetermined Extended-Infomax source model is used to estimate the source signals. As the separation evaluation results are so sensitive to the mixing matrix estimation outcome, achieving nearly optimum results in the first phase of the proposed algorithm significantly improves the results of source separation performance. A weighted version of k-plane clustering algorithm is derived to obtain the mixing coefficients. Experimental evaluations reveal the effectiveness of our proposed method over the state-of-the-art techniques.

A high-resolution minimum variance algorithm based on optimal frequency-domain segmentation

In order to enhance the adaptability of minimum variance (MV) algorithm to broadband ultrasound signals, a high-resolution MV beamforming algorithm based on optimal frequency-domain segmentation is proposed. By an improved time-frequency concentration criterion based on logarithmic window energy, the optimal window length of Short-time Fourier Transform (STFTMV) is obtained, and the ultrasound signals are converted into narrowband sub-signals through optimal frequency-domain segmentation. Then, the sub-signals in frequency domain are reconstructed according to the non-overlapping characteristic of window, which further improves the imaging quality and extends the limitations of traditional broadband MV near-field imaging. Moreover, according to the symmetry of STFTMV, the computation is reduced by half, which further improves the efficiency. The Field II results indicate that the mainlobe width of the STFTMV is reduced to 38.09 % of MV and 47.05 % of eigenspace-based minimum variance (ESBMV), and the imaging efficiency of STFTMV is almost 2.5 times higher than that of ESBMV. In addition, the contrast of the proposed STFTMV can be further improved by combining with the improved coherence factor (FCF) based on sub-band frequency signals.

Classification of sleep apnea based on EEG sub-band signal characteristics

Sleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological changes that occur during apnea, and can be used to diagnose and monitor sleep apnea events. Herein, we proposed a method to automatically distinguish sleep apnea events using characteristics of EEG signals in order to categorize obstructive sleep apnea (OSA) events, central sleep apnea (CSA) events and normal breathing events. Through the use of an Infinite Impulse Response Butterworth Band pass filter, we divided the EEG signals of C3-A2 and C4-A1 into five sub-bands. Next, we extracted sample entropy and variance of each sub-band. The neighbor composition analysis (NCA) method was utilized for feature selection, and the results are used as input coefficients for classification using random forest, K-nearest neighbor, and support vector machine classifiers. After a 10-fold cross-validation, we found that the average accuracy rate was 88.99%. Specifically, the accuracy of each category, including OSA, CSA and normal breathing were 80.43%, 84.85%, and 95.24%, respectively. The proposed method has great potential in the automatic classification of patients' respiratory events during clinical examinations, and provides a novel idea for the development of an automatic classification system for sleep apnea and normal events without the need for expert intervention.

Chern insulators, van Hove singularities and topological flat bands in magic-angle twisted bilayer graphene.

Magic-angle twisted bilayer graphene exhibits intriguing quantum phase transitions triggered by enhanced electron-electron interactions when its flat bands are partially filled. However, the phases themselves and their connection to the putative non-trivial topology of the flat bands are largely unexplored. Here we report transport measurements revealing a succession of doping-induced Lifshitz transitions that are accompanied by van Hove singularities, which facilitate the emergence of correlation-induced gaps and topologically non-trivial subbands. In the presence of a magnetic field, well-quantized Hall plateaus at a filling of 1,2,3 carriers per moiré cell reveal the subband topology and signal the emergence of Chern insulators with Chern numbers, C = 3,2,1, respectively. Surprisingly, for magnetic fields exceeding 5 T we observe a van Hove singularity at a filling of 3.5, suggesting the possibility of a fractional Chern insulator. This van Hove singularity is accompanied by a crossover from low-temperature metallic, to high-temperature insulating behaviour, characteristic of entropically driven Pomeranchuk-like transitions.

A new robust delayless subband adaptive filtering algorithm with variable step sizes for active control of broadband noise

Conventional delayless subband active noise control (ANC) systems can be degraded by the unexpected impulsive disturbances in the reference signal and/or the residual error signals despite the fact that the step sizes have been normalized by the variances of subband signals. Another inherent drawback is that an undesirable delay is introduced into the weight update path by using analysis filter banks, which reduces the upper bound of the step sizes thus limits the convergence rate. This paper proposes a novel robust and effective ANC algorithm configured in the delayless subband architecture for broadband noise cancellation. Instead of real-time providing threshold(s) on the reference and/or error signal as applied in most of the existing ANC algorithms for robustness, an online tuning scheme of bounded step sizes for the adaptive learning is developed for better convergence and outlier suppression. Box-constraint and time-averaging scheme deployed in the step size tuning guarantee robustness without requiring very accurate a priori information of the noises which might be corrupted by the impulses in real-world applications. In particular, this paper presents the stability and convergence analysis of the proposed closed-loop ANC system. Moreover, the computational complexity advantage is justified when compared to other state-of-the-art robust ANC algorithms. Numerical simulations are performed to validate the enhanced performance of the proposed algorithm for various colored noises with or without impulsive interferences.

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

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> -band between contaminated and cleaned EEG signal, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> SNR, percentage change in correlation coefficients ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula> ), 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> SNR, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula> , and MAE-PSD values of 30.59 dB, 68.76%, and 0.0263 dB/Hz in comparison to the multiresolution total variation (MTV) ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> SNR as 29.12 dB, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> -bands of EEG signals recorded during standing and walking conditions.

Trial Regeneration With Subband Signals for Motor Imagery Classification in BCI Paradigm

Electroencephalography (EEG) captures the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imager (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG trial is regenerated using narrowband signals obtained from individual channel. Each channel of EEG trial is decomposed into a set of subband signals using multivariate discrete wavelet transform. The selected subbands are organized in two different ways namely vertical arrangement of subbands (VaS) and horizontal arrangement of subbands (HaS) to regenerate the trials. The features are extracted from each of the arrangements using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. The effectiveness of two classifiers– linear discriminant analysis (LDA) and support vector machine (SVM) are studied. The performances of the proposed methods are evaluated using publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.

An efficient wide-band signal detection and extraction method

Aiming at the influence of time-varying and frequency-varying of noise on the signal detection performance in the short wave wide-band channel and the large amount of computation in the channelized receiver model of the traditional low pass filter bank, a cross-channel reconfigurable multi-phase high-efficiency channelization method based on morphological processing is proposed in this paper .Firstly, The wide-band signal is coarsely filtered by the multi-phase structure of the uniform filter bank which is determined by the protection interval between signals, and then the bandwidth and position of the signal are determined by improved morphological operation and threshold decision of the power spectrum. Finally, the sub-band signals across the channel are combined to complete the approximate reconstruction of the sub-signals. Compared with the computational complexity of traditional channelized receiver model, the results show that this method has lower computational complexity. The simulation results show that the method can achieve the approximate constant false alarm rate(CFAR) under the colored noise environment, and has higher detection capability under different signal-to-noise ratios(SNR).

Research on early warning of rolling bearing wear failure based on empirical mode decomposition

In order to solve the problems of low precision and long time-consuming of traditional methods, this paper designs a rolling bearing wear fault early warning method based on empirical mode decomposition (EMD). Based on the wear reason of rolling bearing, the acceleration sensor is used to collect its vibration signal, and the EMD algorithm is used to stabilise the signal to obtain multi-scale signal. Each multi-scale signal is decomposed into sub-band to get multi-scale sub-band signal, then the multi-scale sub-band sample entropy is obtained, and the optimisation function of local preserving projection algorithm is constructed to obtain the eigenvalue and eigenvector of wear failure fault, and finally the fault early warning is realised. The simulation results show that the signal denoising effect of this method is good, the early warning accuracy is always above 94%, and the average alarm time is close to 0.27 s.

Sub-band selection approach to artifact suppression from electroencephalography signal using hybrid wavelet transform

This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The hybrid wavelet transform (HWT) method is designed by the combination of discrete wavelet decomposition and wavelet packet transform. The artifact suppression is performed by the selection of sub-bands obtained by HWT. Fractional Gaussian noise (fGn) is used as the reference signal to select the sub-bands containing the artifacts. The multichannel EEG signal is decomposed HWT into a finite set of sub-bands. The energies of the sub-bands are compared to that of the fGn to the desired sub-band signals. The EEG signal is reconstructed by the selected sub-bands consisting of EEG. The experiments are conducted for both simulated and real EEG signals to study the performance of the proposed algorithm. The results are compared with recently developed algorithms of artifact suppression. It is found that the proposed method performs better than the methods compared in terms of performance metrics and computational cost.

Feature Extraction Using Hierarchical Dispersion Entropy for Rolling Bearing Fault Diagnosis

Effective feature extraction is crucial for accurate fault diagnosis of rolling bearings. A novel feature extraction method called hierarchical dispersion entropy (HDE) based on hierarchical analysis is proposed in this study. The proposed method includes the following three steps: 1) bearing vibration signal is decomposed into a series of subband signal components; 2) dispersion entropies of the components in different frequency bands are calculated as the original feature vector; and 3) joint approximate diagonalization of eigenmatrices (JADE) is used to extract fusion features from the original features. The main contributions of the proposed method are as follows: 1) the HDE method can characterize the complexity and uncertainty of the signal in full frequency band; 2) the JADE method further eliminates redundant information while greatly retaining fault-relevant information; and 3) the proposed method combines the advantages of HDE's hierarchical analysis and JADE's information fusion capabilities, so the fusion features extracted by the proposed method can be more effective for the establishment of fault pattern identification model. In the analysis of two experimental cases, the feature extracted by the proposed method shows better feature clustering effect and higher recognition rate than other methods. The results show that compared with other methods, the proposed method can more accurately characterize the health condition of the bearing.

Removal of Dust Microelectric Signal Based on Empirical Mode Decomposition and Multifractal Detrended Fluctuation Analysis.

Microcharge induction has recently been applied as a dust detection method. However, in complex environments, the detection device can be seriously polluted by noise. To improve the quality of the measured signal, the characteristics of both the signal and the noise should be analyzed so as to determine an effective noise removal method. Traditional removal methods mostly deal with specific noise signals, and it is difficult to consider the correlation of measured signals between adjacent time periods. To overcome this shortcoming, we describe a method in which wavelet decomposition is applied to the measured signal to obtain sub-band components in different frequency ranges. A time-lapse Pearson method is then used to analyze the correlation of the sub-band components and the noise signal. This allows the sub-band component of the measurement signal that has the strongest correlation with the noise to be determined. Based on multifractal detrended fluctuation analysis combined with empirical mode decomposition, the similarity between the signal sub-band components and the noise sub-band components is analyzed and three indices are employed to determine the multifractal characteristics of the sub-band components. The consistency between noise components and signal components is obtained and the main signal components are verified. Finally, the sub-band components are used to reconstruct the signal, giving the noise-free measured (microcharge induction) signal. The filtered signal presents smoother, multifractal features.

New efficient subband structures for blind source separation

Convolutive mixtures of signals generated by more than one source and acquired by a set of microphones are commonly found in acoustic signal processing applications. Subband methods have been proposed to reduce computational complexity and improve the convergence rate of adaptive algorithms developed for blind source separation of these mixtures, without significantly impairing steady-state performance. Oversampled discrete Fourier transform (DFT) filter bank is a usual option for the generation of subband signals in order to avoid harmful aliasing effects, thereby maintaining sufficient samples to estimate the subband signal statistics. In this paper, two new subband structures, composed of cosine-modulated filter banks (CMFB) with critical or oversampled sampling and low-order adaptive subfilters, are proposed for efficient blind source separation approach in convolutive mixtures of speech signals. A time-varying step-size procedure that provides better convergence rate for several reverberation characteristics is advanced. In addition, both computational complexity and steady-state performance of the proposed structures are compared to those of the standard fullband algorithm, to other subband structures with oversampling and critical sampling, and to frequency domain algorithm. The advanced solutions are capable of improving the source interference ratio (SIR) by more than 5 dB. Finally, two strategies are presented to minimize the effects of remaining aliasing in the proposed subband approaches, which obtain an additional gain of about 3 dB in the asymptotic SIR.

Adaptive correlated Kurtogram and its applications in wheelset-bearing system fault diagnosis

As one of the crucial subsystems, the performance of the wheelset-bearing system will significantly affect the running safety, stability and comfort of a high-speed train. Compared with the traditional rotating machinery vibration signals, those of wheelset-bearing systems have a distinct difference, for instance, the wheelset tread damage induced vibrations and bearing fault-related vibrations coexist and have similar wave-forms. Therefore, it is necessary to advance a reasonable method to identify the two kinds of cyclo-stationary components. To achieve this purpose, the adaptive correlated Kurtogram (ACK) is proposed. The proposed ACK method has two advantages: the first one is it can adaptively generate the paving of the plane for Kurtogram by using scale-space representation (SSR) to guarantee the detected boundaries cover the resonant frequency band well; the second one is it can highlight the special periodic impulses by correlated kurtosis. The scale-scale plane (SSP), which is obtained by SSR, is used to represent the frequency distribution characteristic of the acquired signal, firstly. After that, the paving of the plane is arbitrarily divided based on the SSP. Then the empirical wavelet transform (EWT) is employed to construct the sub-band signals according to the paving of the plane. The correlated kurtosis values with interesting periodic are calculated to distinguish the special repetitive transients. The nodes with the largest correlated kurtosis values are used to conduct a further envelope spectrum analysis. ACK method improves the effectiveness of the identification of the frequency bands containing impact information, and is capable of detecting multiple frequency bands simultaneously. The proposed ACK method is demonstrated by the simulated signal and two experimental signals acquired from a wheelset-bearing system test bench. The results indicate that the ACK can simultaneously extract the wheelset tread damage and bearing fault-related vibration component, and it is superior to the other traditional methods.

Penalized Functional Connectivity Maps for Patients With Focal Epilepsy

Objective : This study demonstrates how functional connectivity (FC) patterns are affected in direct relation to the lobe that is mostly affected by seizures. Methods : The novel idea of penalized FC (pFC) maps is compared against standard FC maps in the four fundamental EEG frequency sub-bands. The FC measure between any two specific electrodes is scaled depending on the probability of true FC between them, and their power content with respect to the two electrodes of maximum power within each frequency sub-band. The algorithm is automated and introduces adaptive power penalization based on the power distribution of the different sub-bands. Results : The pFC maps were found to be more effective at suppressing the local connectivity in the lobes that are less affected by the interictal epileptiform discharges (IEDs). More precisely, given the least amount of power penalization, pFC maps of the theta sub-band reveal statistical significance in terms of increased local connectivity margin of the affected region as compared to the standard FC maps. However, they cannot be solely relied upon as other sub-bands could alternatively show high local connectivity across different patients within the region of interest. Conclusion : penalized functional connectivity maps intrinsically provide more information regarding the whole brain network in context to regions of interest where the active lobe is determined by the neurologists to contain the focal source. Significance : Findings suggest that (1) the significant sub-band varies from patient to patient while remaining relatively consistent within the IED segments of a same patient, and (2) the pFC maps have an advanced capability in terms of pinpointing to a region of interest of the active lobe, and as such can play a critical role in providing insight as to a region of interest where the 3D source might be located when solving the ill-posed inverse problem.

Artificial EEG signal generated by a network of neurons with one and two dendrites

The electroencephalogram EEG signal analysis is being strongly explored as a new potential tool for control, communication, and clinical diagnosis applications related to many neurological pathologies such as epilepsy, autism, Alzheimer. Analyzing EEG data is a very interesting approach to study cognitive processes. Thus, it can help researchers to understand the brain processes, doctor to establish an efficient medical diagnosis. To better understand the nature and the behavior of this signal which seems to be complex, nonlinear, non-stationary, and imbalanced, we propose to generate this signal using a predefined model of neurons with one and two dendrites by studying their activation functions. Then, we build a network of neurons with two dendrites correlated with the α and β frequencies which represent the most significant sub-bands in EEG signals to generate a signal quite close to EEG recordings. Through this paper, we aim to present a comprehensive study and an exploration of artificial EEG signal generation using only neurons with two dendrites that provided different activation and excitation functions. The presented result shows that the generated EEG signal with six neurons of two dendrites is quite similar to the real EEG recording for a healthy subjects. In addition, some artifacts like Alzheimer and Parkinson have been emulated and identified among the generated spikes. Finally we proved that we can eliminate some artifacts related to harmonics by applying activation function with opposite polarities.

Multi-Scale Demodulation for Fault Diagnosis Based on a Weighted-EMD De-Noising Technique and Time–Frequency Envelope Analysis

Demodulation is one of the most useful techniques for the fault diagnosis of rotating machinery. The commonly used demodulation methods try to select one sensitive sub-band signal that contains the most fault-related components for further analysis. However, a large number of the fault-related components that exist in other sub-bands are ignored in the commonly used envelope demodulation methods. Based on a weighted-empirical mode decomposition (EMD) de-noising technique and time–frequency (TF) impulse envelope analysis, a multi-scale demodulation method is proposed for fault diagnosis. In the proposed method, EMD is first employed to divide the signal into some IMFs (intrinsic mode functions). Then, a new weighted-EMD de-noising technique is presented, and different weights are assigned to IMFs for construction according to their fault-related degrees; thus, the fault-unrelated components are suppressed to improve the signal-to-noise ratio (SNR). After that, continuous wavelet transformation (CWT) is adopted to obtain the time–frequency representation (TFR) of the de-noised signal. Subsequently, the fault-related components in the entire frequency range scale are calculated together, referring to the TF impulse envelope signal. Finally, a fault diagnosis result can be obtained after the fast Fourier transformation of the TF impulse envelope signal. The proposed method and three commonly used methods are applied to the fault diagnosis of a planetary gearbox with a sun gear spalling fault and a fixed shaft gearbox with a crack fault. The results show that the proposed method can effectively detect gear faults and yields better performance than other methods.