Traditional Independent Component Analysis Research Articles

Fault detection in multimodal processes based on the local entropy double subspace

Recently, principal component analysis (PCA) and independent component analysis (ICA) have attracted increasing attention for fault detection in industrial processes. However, industrial processes usually have multimodal characteristics, in which case, the performance of traditional PCA and ICA methods is poor. A fault detection method based on the local entropy double subspace (LEDS) is proposed that effectively supervises the multimodal process with multiple steady modes. The proposed method uses the local probability density (LPD) to eliminate the multimodal characteristics of the data. Furthermore, local entropy (LE) is applied to mine the statistical information associated with the process data. The process variables are classified into Gaussian and non-Gaussian subspaces by the Kolmogorov–Smirnov (KS) test. PCA and ICA models are built in Gaussian and non-Gaussian subspaces, respectively, for fault detection. A Bayesian decision method is employed to transform the detection results into fault probabilities, and the detection results are combined with the final statistical information. This method effectively eliminates the multicentre structure of the data and improves the fault detection performance of traditional PCA and ICA. The proposed method is applied to the Tennessee Eastman multimodal process. The simulation results show that the fault detection rate of LEDS is better than the rates of traditional methods when the false alarm rate is low for most faults.

Unique estimation in EEG analysis by the ordering ICA.

Independent Component Analysis (ICA) is a method for solving blind source separation problems. Because ICA only needs weak assumptions to estimate the unknown sources from only the observed signals, it is suitable for Electroencephalography (EEG) analysis. A serious disadvantage of the traditional ICA algorithms is that their results often fluctuate and do not converge to the unique and globally optimal solution at each run. It is because there are many local optima and permutation ambiguities. We have recently proposed a new ICA algorithm named the ordering ICA, a simple extension of Fast ICA. The ordering ICA is theoretically guaranteed to extract the independent components in the unique order and avoids the local optima in practice. This paper investigated the usefulness of the ordering ICA in EEG analysis. Experiments showed that the ordering ICA could give unique solutions for the signals with large non-Gaussianity, and the ease of parallelization could reduce computation time.

Research on Music Signal Processing Based on a Blind Source Separation Algorithm

The isolation of mixed music signals is beneficial to the extraction and identification of music signal features and to enhance music signal quality. This paper briefly introduced the mathematical model for separating blind source from mixed music signals and the traditional Independent Component Analysis (ICA) algorithm. The separation algorithm was optimized by the complex neural network. The traditional and optimized ICA algorithms were simulated in MATLAB software. It was found that the time-domain waveform of the signal isolated by the improved ICA-based separation algorithm was closer to the source signal. The similarity coefficient matrix, signal-to-interference ratio, performance index, and iteration time of the improved ICA-based algorithm was 62.3, 0.0011, and 0.87 s, respectively, which were all superior to the traditional ICA algorithm. The novelty of this paper is setting the initial iterative matrix of the ICA algorithm with the complex neural network.

Snowball ICA: A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data.

In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order selection (MOS) algorithms have been used to determine the number of estimated components. However, simulations show that even when the model order equals the number of simulated signal sources, traditional ICA algorithms may misestimate the spatial maps of the signal sources. In principle, increasing model order will consider more potential information in the estimation, and should therefore produce more accurate results. However, this strategy may not work for fMRI because large-scale networks are widely spatially distributed and thus have increased mutual information with noise. As such, conventional ICA algorithms with high model orders may not extract these components at all. This conflict makes the selection of model order a problem. We present a new strategy for model order free ICA, called Snowball ICA, that obviates these issues. The algorithm collects all information for each network from fMRI data without the limitations of network scale. Using simulations and in vivo resting-state fMRI data, our results show that component estimation using Snowball ICA is more accurate than traditional ICA. The Snowball ICA software is available at https://github.com/GHu-DUT/Snowball-ICA.

Pre-whitening and Null Projection as an Artifact Suppression Method for Electrocorticography Stimulation in Bi-Directional Brain Computer Interfaces.

Electrocorticography (ECoG)-based bi-directional (BD) brain-computer interfaces (BCIs) are a forthcoming technology promising to help restore function to those with motor and sensory deficits. A major problem with this paradigm is that the cortical stimulation necessary to elicit artificial sensation creates strong electrical artifacts that can disrupt BCI operation by saturating recording amplifiers or obscuring useful neural signal. Even with state-of-the-art hardware artifact suppression methods, robust signal processing techniques are still required to suppress residual artifacts that are present at the digital back-end. Herein we demonstrate the effectiveness of a pre-whitening and null projection artifact suppression method using ECoG data recorded during a clinical neurostimulation procedure. Our method achieved a maximum artifact suppression of 21.49 dB and significantly increased the number of artifact-free frequencies in the frequency domain. This performance surpasses that of a more traditional independent component analysis methodology, while retaining a reduced complexity and increased computational efficiency.

Online Recursive ICA Algorithm Used for Motor Imagery EEG Signal.

Electroencephalogram (EEG) signals are important to study the activities of human brains. The independent component analysis (ICA) algorithm is a practical blind source separation (BSS) technique that can separate EEG sources from artifacts effectively. However, most traditional ICA algorithms assume that the mixing process is instantaneous and off-line. In this paper, a novel framework based on the extension of the online recursive ICA algorithm (ORICA) is proposed to apply for motor imagery (MI) EEG recording. The contributions are as follows. Firstly, we show ORICA's adaptability to accurate and effective source separation used for artifact-contaminated MI EEG recording. Secondly, to identify EOG signals on the output of source separation, the topographic map is presented to distinguish the target signals. The experimental results show that the proposed framework is able to be applied to process MI EEG recording in real-time situations.

Enhanced forensic speaker verification performance using the ICA-EBM algorithm under noisy and reverberant environments

Forensic speaker verification performance reduces significantly under high levels of noise and reverberation. Multiple channel speech enhancement algorithms, such as independent component analysis by entropy bound minimization (ICA-EBM), can be used to improve noisy forensic speaker verification performance. Although the ICA-EBM was used in previous studies to separate mixed speech signals under clean conditions, the effectiveness of using the ICA-EBM for improving forensic speaker verification performance under noisy and reverberant conditions has not been investigated yet. In this paper, the ICA-EBM algorithm is used to separate the clean speech from noisy speech signals. Features from the enhanced speech are obtained by combining the feature-warped mel frequency cepstral coefficients with similar features extracted from the discrete wavelet transform. The identity vector (i-vector) length normalized Gaussian probabilistic linear discriminant analysis is used as a classifier. The Australian Forensic Voice Comparison and QUT-NOISE corpora were used to evaluate forensic speaker verification performance under noisy and reverberant conditions. Simulation results demonstrate that forensic speaker verification performance based on ICA-EBM improves compared with that of the traditional independent component analysis under different types of noise and reverberation environments. For surveillance recordings corrupted with different types of noise (CAR, STREET and HOME) at − 10 dB signal to noise ratio, the average equal error rate of the proposed method based on ICA-EBM is better than that of the traditional ICA by 12.68% when the interview recordings are kept clean, and 7.25% when the interview recordings have simulated room reverberations.

Frontoparietal and salience network alterations in obsessive–compulsive disorder: insights from independent component and sliding time window analyses

Resting-state functional MRI (fMRI) studies commonly report alterations in 3 core networks in obsessive–compulsive disorder (OCD) — the frontoparietal network, the default mode network and the salience network — defined by functionally connected infraslow oscillations in ongoing brain activity. However, most of these studies observed static functional connectivity in the brains of patients with OCD. To investigate dynamic functional connectivity alterations and widen the evidence base toward the triple network model in OCD, we performed group-based independent component and sliding time window analyses in 49 patients with OCD and 41 healthy controls. The traditional independent component analysis showed alterations in the left frontoparietal network as well as between the left and right frontoparietal networks in patients with OCD compared with healthy controls. For dynamic functional connectivity, the sliding time window approach revealed peak dysconnectivity between the left and right frontoparietal networks and between the left frontoparietal network and the salience network. The number of independent components, noise in the resting-state fMRI images, the heterogeneity of the OCD sample, and comorbidities and medication status in the patients could have biased the results. Disrupted modulation of these intrinsic brain networks may contribute to the pathophysiology of OCD.

Minimum BER Criterion and Adaptive Moment Estimation Based Enhanced ICA for Wireless Communications

This paper concentrates on investigating an enhanced independent component analysis (ICA) method for blind separation of signals corrupted by noise in wireless communications. Because of the traditional classical ICA methods that always have an inadequate capacity of anti-noise or insufficient separable ability in noise circumstance without satisfying practical application requirements. For this reason, two mechanisms are conducted to establish the modified cost function and fulfill the optimization assignment in the process of constituting an enhanced ICA algorithm. This proposed algorithm can benefit tremendously from the derived minimum bit error rate (BER) criterion and the novel adaptive moment estimation (Adam) optimization approach. In this work, firstly, the novel cost function is obtained according to minimum BER fused into maximum likelihood (ML) principle-based ICA cost function. Furthermore, by utilizing Adam processing, the task of blind separation of mixed signals is implemented via optimizing this modified cost function. Lastly, theoretical analysis and experiment results corroborate the better effectiveness and robustness of the proposed enhanced ICA algorithm compared with a series of popular representative ICA methods.

Monitoring multivariate process using improved Independent component analysis-generalized likelihood ratio strategy

Though the automation in modern process plants has reduced overall functioning cost, monitoring process for detecting abnormalities is still a challenging task. In this direction, data driven fault detection (FD) methods are commonly applied as they rely purely on historical data collected from a process. Since most practical data are non-gaussian in nature, Independent Component Analysis (ICA) strategy has found to be the right way for non-gaussian process monitoring. To determine faults, generalized likelihood ratio test (GLRT) has been merged with other data driven approaches. In this paper, we propose a FD strategy where ICA is used as modeling framework and generalized likelihood ratio test (GLR) as a fault detection index. Once ICA model is established, uncorrelated residuals obtained from the model would be evaluated by the GLR test to detect faults. The monitoring performance is compared with traditional ICA based fault indicators- Id2, Ie2 and SPE statistics. In addition to fault detection, fault isolation strategy is also presented in this work. The proposed ICA based GLR strategy performance is evaluated using a simulated quadruple tank process and benchmark Tennessee Eastman process. The simulation results shows that the proposed fault detection approach is able to detect sensor faults very effectively.

Analysis of the regional GNSS coordinate time series by ICA-weighted spatio-temporal filtering

Independent component analysis (ICA) is a blind source signal separation method which can effectively estimate high-order information and thus can effectively extract the common mode errors (CMEs) of a regional global navigation satellite system (GNSS) observation network. In this paper, ICA is used for the weighted filtering (WICA) and the extraction of CMEs of a regional GNSS observation network with the root mean square error (RMSE) of daily solution taken as the weighting factor. Through an analysis of the observed data from 19 valid stations of the Crustal Movement Observation Network of China (CMONOC) in North China, it is shown that the coordinate series precision of 13, 16 and 12 stations in the N, E and U directions, respectively, after filtering by WICA is higher than that by the traditional ICA method. The average correlation coefficient of the coordinate time series for each station after filtering is obviously decreased. Two simulation experiments are designed to extract known CMEs. It is shown that CMEs can be recovered better by WICA and that the standard deviations of most stations after filtering are smaller than those by ICA. The results from the real data and simulation experiments suggest that the RMSE of coordinate series be considered in spatio-temporal filtering.

Multi-band component analysis for EEG artifact removal and source reconstruction with application to gamma-band activity

Independent component analysis (ICA) has been used extensively for artifact removal and reconstruction of neuronal time-courses in electroencephalography (EEG). Typically, ICA is applied on wide-band EEG (for example 1 to 100 Hz or similar ranges). EEG captures the activities of a large number of sources and the as the number of the components separated by the ICA is limited by the number of the sensors, only the stronger sources (in terms of magnitude and duration) will be detected by the ICA, and the activity of weaker sources will be lost or scattered amongst the stronger components. Because of the 1/f nature of the EEG spectra this biases components to the lower frequency ranges. Here we used multi-band ICA; a versatile combination of a filter bank, PCA, and ICA, to increase the number of the ICA components substantially and improve the SNR of the separated components. Using band-pass filtering we break the original signal mixture into several subbands, then using PCA we reduce the dimensionality of each subband, before applying ICA to a matrix containing all the principal components from each band. Using simulated sources and real EEG from participants, we demonstrate that multi-band ICA is able to outperform the traditional wide-band ICA in terms of both signal-to-noise ratio of the separated sources and the number of the identified independent components. We successfully separated the gamma-band neuronal components that were time-locked to a visual stimulus, as well as weak sources which are not detectable by wide-band ICA.

Voltage Flicker Analysis Based on Improved Independent Component Analysis

The traditional independent component analysis uses Newton method to optimize its cost function, so it always falls into local optimal solution. This affects its application in voltage flicker detection. To improve the performance of voltage flicker detection based on independent component analysis, this paper uses artificial bee colony algorithm that has global optimization to optimize the cost function of independent component analysis. Simulations prove that the improved voltage flicker detection method has better performance than the original method.

A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets.

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.

Independent Component Analysis Method Based on Genetic Algorithm in Compound Fault Separation

Compound faults often exist in roller bearing, which increase the difficulty for the fault diagnosis. In order to extract the characteristics of compound fault signals, independent component analysis (ICA) method was used toseparate fault signals. However, the selection of initial weight about ICA will affect the number of iterations and astringency. Therefore, a novel ICA method based on improved genetic algorithm was proposed. The kurtosis ofsignal was used as the optimization function, and then genetic algorithm was applied to find the separation matrix according to the maximum of the kurtosis. This method avoids the problem of redundant iteration and convergence, which is caused by randomness of initial weight vector. Finally, the proposed method was used to separate source signal from the mixed signals and achieve the roller bearings fault identification and separation. The results show that the proposed method is superior to traditional ICA method, and the compound fault can be separated based on the proposed method.

Non-Gaussian Industrial Process Monitoring With Probabilistic Independent Component Analysis

Independent component analysis (ICA) is widely used for modeling and monitoring non-Gaussian process. However, traditional ICA lacks probabilistic representation of process uncertainties. In this study, a probabilistic ICA (PICA) model is proposed for non-Gaussian process modeling and monitoring. The independent latent spaces are specified with Student’s <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm t}$</tex></formula> formulation to account for both Gaussian and non-Gaussian data characteristics while the additional noise term is further served as a complement for explaining underlying process uncertainties. The Student’s <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm t}$</tex></formula> distribution with adjustable tails is essentially an infinite mixture of Gaussians with various scaling variances. In order to monitor retained variations, the noise space is further extracted and analyzed with probabilistic principal component analysis (PPCA). Simulation results show that compared with the deterministic ICA-based method, the proposed two-stage probabilistic extraction method is more effective for monitoring non-Gaussian industrial processes.

A new method for independent component analysis with priori information based on multi-objective optimization

BackgroundCurrently the problem of incorporating priori information into an independent component analysis (ICA) model is often solved under the framework of constrained ICA, which utilizes the priori information as a reference signal to form a constraint condition and then introduce it into classical ICA. However, it is difficult to pre-determine a suitable threshold parameter to constrain the closeness between the output signal and the reference signal in the constraint condition. New methodIn this paper, a new model of ICA with priori information as a reference signal is established on the framework of multi-objective optimization, where an adaptive weighted summation method is introduced to solve this multi-objective optimization problem with a new fixed-point learning algorithm. ResultsThe experimental results of fMRI hybrid data and task-related data on the single-subject level have demonstrated that the proposed method has a better overall performance on the recover abilities of both spatial source and time course. Comparison with existing methodsAt the same time, compared with traditional ICA with reference methods and classical ICA method, the experimental results of resting-state fMRI data on the group-level have showed that the group independent component calculated by the proposed method has a higher correlation with the corresponding independent component of each subject through T-test. ConclusionsThe proposed method does not need us to select a threshold parameter to constrain the closeness between the output signal and the reference signal. In addition, the performance of functional connectivity detection has a great improvement in comparison with traditional methods.

Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation

The estimation of oil spill coverage is an important part of monitoring of oil spills at sea. The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills and the accuracy of estimates of their size. We consider at-sea oil spills with zonal distribution in this paper and improve the traditional independent component analysis algorithm. For each independent component we added two constraint conditions: non-negativity and constant sum. We use priority weighting by higher-order statistics, and then the spectral angle match method to overcome the order nondeterminacy. By these steps, endmembers can be extracted and abundance quantified simultaneously. To examine the coverage of a real oil spill and correct our estimate, a simulation experiment and a real experiment were designed using the algorithm described above. The result indicated that, for the simulation data, the abundance estimation error is 2.52% and minimum root mean square error of the reconstructed image is 0.030 6. We estimated the oil spill rate and area based on eight hyper-spectral remote sensing images collected by an airborne survey of Shandong Changdao in 2011. The total oil spill area was 0.224 km2, and the oil spill rate was 22.89%. The method we demonstrate in this paper can be used for the automatic monitoring of oil spill coverage rates. It also allows the accurate estimation of the oil spill area.

Fault detection of multimode non-Gaussian dynamic process using dynamic Bayesian independent component analysis

Independent component analysis (ICA) has been widely used in non-Gaussian multivariate process monitoring. However, it assumes only one normal operation mode and omits the dynamic characteristic of process data. In order to overcome the shortcomings of traditional ICA based fault detection method, an improved ICA method, referred to as dynamic Bayesian independent component analysis (DBICA), is proposed to monitor the multimode non-Gaussian dynamic process. In this method, matrix dynamic augmentation is applied to extract dynamic information from original data. Then for analyzing multimode non-Gaussian data, Bayesian inference and ICA are combined to establish a probability mixture model. The ICA model parameters are obtained by the iterative optimization algorithm and the mode of each observation is determined by Bayesian inference simultaneously. Lastly case studies on one continuous stirring tank reactor (CSTR) simulation system and the Tennessee Eastman (TE) benchmark process are used to demonstrate the effectiveness of the proposed method.

Independent component analysis model utilizing de-mixing information for improved non-Gaussian process monitoring

The de-mixing matrix generated from independent component analysis (ICA) can reveal information about the relations between variables and independent components, but the traditional ICA model does not preserve the whole de-mixing information for the purpose of feature extraction and dimensionality reduction, so that some important information may be abandoned. Multi-block strategy has been improved to be an efficient method to deal with numerous data. However, the manner of dividing original data is still subject for discussion and the priori knowledge is necessary for process division. This paper proposes a totally data-driven ICA model that divides de-mixing matrix based on the Generalized Dice’s coefficient and combines the results from sub-blocks using Bayesian inference. All information in de-mixing matrix is fully utilized and the ability of monitoring non-Gaussian process is improved. Meanwhile, a corresponding contribution plot is developed for fault diagnosis to find the root causes. The performance of the proposed method is illustrated through a numerical example and the Tennessee Eastman benchmark case study.