ICA Method Research Articles

Spectral Independent Component Analysis with noise modeling for M/EEG source separation

BackgroundIndependent Component Analysis (ICA) is a widespread tool for exploration and denoising of electroencephalography (EEG) or magnetoencephalography (MEG) signals. In its most common formulation, ICA assumes that the signal matrix is a noiseless linear mixture of independent sources that are assumed non-Gaussian. A limitation is that it enforces to estimate as many sources as sensors or to rely on a detrimental PCA step. MethodsWe present the Spectral Matching ICA (SMICA) model. Signals are modelled as a linear mixing of independent sources corrupted by additive noise, where sources and the noise are stationary Gaussian time series. Thanks to the Gaussian assumption, the negative log-likelihood has a simple expression as a sum of ‘divergences’ between the empirical spectral covariance matrices of the signals and those predicted by the model. The model parameters can then be estimated by the expectation-maximization (EM) algorithm. ResultsOn phantom MEG datasets with low amplitude dipole sources (20 nAm), SMICA makes a median dipole localization error of 1.5 mm while competing methods make an error ≥7 mm. Experiments on EEG datasets show that SMICA identifies a source subspace which contains sources that have less pairwise mutual information, and are better explained by the projection of a single dipole on the scalp. With 10 sources, the number of strongly dipolar sources (dipolarity >90%) is more than 80% for SMICA while competing methods do not exceed 65%. Comparison with existing methodsWith the noisy model of SMICA, the number of sources to be recovered is controlled by choosing the size of the mixing matrix to be fitted rather than by a preprocessing step of dimension reduction which is required in traditional noise-free ICA methods. ConclusionsSMICA is a promising alternative to other noiseless ICA models based on non-Gaussian assumptions.

Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain

Finding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.

Development of immunochromatographic strips for the detection of dicofol

In this study, a monoclonal antibody (mAb) against dicofol was developed to prepare immunochromatographic strips (ICAs) for the detection of dicofol residues in fruit and vegetables. The mAb exhibited high affinity and high sensitivity, with an affinity constant of 2.96 × 1010 and a limit of detection of 3.142 ng mL-1. A cross reactivity test revealed that the mAb also had good specificity for dicofol. This ICA method gave a visible limit of detection of 50 ng g-1, and a cut-off value of 500 ng g-1 for the detection of dicofol in both apple and cucumber with the naked eye. Importantly, the results here are consistent with results obtained using liquid chromatography mass spectrometry and ic-ELISAs indicating that this ICA method is reliable and practical when used for the detection of dicofol in fruit and vegetables.

Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach

Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries.

Estimation of simultaneous BOLD and dynamic FDG metabolic brain activations using a multimodality concatenated ICA (mcICA) method

Simultaneous magnetic resonance and positron emission tomography provides an opportunity to measure brain haemodynamics and metabolism in a single scan session, and to identify brain activations from multimodal measurements in response to external stimulation. However, there are few analysis methods available for jointly analysing the simultaneously acquired blood-oxygen-level dependant functional MRI (fMRI) and 18-F-fluorodeoxyglucose functional PET (fPET) datasets. In this work, we propose a new multimodality concatenated ICA (mcICA) method to identify joint fMRI-fPET brain activations in response to a visual stimulation task. The mcICA method produces a fused map from the multimodal datasets with equal contributions of information from both modalities, measured by entropy. We validated the method in silico, and applied it to an in vivo visual stimulation experiment. The mcICA method estimated the activated brain regions in the visual cortex modulated by both BOLD and FDG signals. The mcICA provides a fully data-driven analysis approach to analyse cerebral haemodynamic response and glucose uptake signals arising from exogenously induced neuronal activity.

A Novel Method for Video Moving Object Detection Using Improved Independent Component Analysis

Moving object detection is a hot and difficult problem. How to separate the changed part from the unchanged one is the key. Also, the connection between the multi-dimensional information of input frames often plays an important role in moving object detection. In this article, we presented a novel and effective video moving object detection method. The innovation of this method lies in the calculation of the connection between multi-dimensional information considering time and space domain at the same time. At present, most moving object detection algorithms consider the time dimension and the space dimension in an independent manner. It is possible to cause some detection problems, such as, if the background information of a static or low-speed object is updated too quickly, only part of the foreground region is detected correctly. To avoid such deficiencies, the improved ICA method is adopted to separate the continuous multi-frame images in the time domain, and then to integrate these signals in the space domain. It could separate and detect the difference between the input signals to extract the correct foreground target, especially in some situations that insignificant changes happen. The proposed algorithm is named VTD-FastICA. A large number of experiments have shown that our method is more competitive than most of the moving object detection methods, especially in detecting long-span, occlusion environments, and objects with weak changes. Moreover, we also analyze the efficiency of our method in different situations, indicating that the proposed algorithm can be applied to real situations.

Vibration reduction of a flexible robot link using a frictional damper

One of the most important factors reducing flexible manipulator efficiency is the residual vibration occurrence. In this research, vibration reduction of flexible manipulators is investigated using an internal frictional damper. At first, the vibration equation of a manipulator is obtained using the finite element method with the Euler–Bernoulli beam element to study its vibrations in a reciprocal motion. In addition, an analytical model is developed to investigate the effect of the frictional damper on robot link vibrations. Using particle swam optimization, ICA, NSGA-II, and GWO methods, the optimal structure for the damper is obtained to maximize its effect. The optimally damped link is fabricated, and its dynamic characteristics are extracted from a modal test experiment. The modal test results show a considerable improvement in the damping ratio of the damped link in comparison with a simple link. The fabricated link samples are then tested in a realistic situation. The experimental results are in coincidence with the simulation results, certifying the performance of the proposed plan in vibration reduction of a robot link.

HINT: A hierarchical independent component analysis toolbox for investigating brain functional networks using neuroimaging data

BackgroundIndependent component analysis (ICA) is a popular tool for investigating brain organization in neuroscience research. In fMRI studies, an important goal is to study how brain networks are modulated by subjects’ clinical and demographic variables. Existing ICA methods and toolboxes don’t incorporate subjects’ covariates effects in ICA estimation of brain networks, which potentially leads to loss in accuracy and statistical power in detecting brain network differences between subjects’ groups. New methodWe introduce a Matlab toolbox, HINT (Hierarchical INdependent component analysis Toolbox), that provides a hierarchical covariate-adjusted ICA (hc-ICA) for modeling and testing covariate effects and generates model-based estimates of brain networks on both the population- and individual-level. HINT provides a user-friendly Matlab GUI that allows users to easily load images, specify covariate effects, monitor model estimation via an EM algorithm, specify hypothesis tests, and visualize results. HINT also has a command line interface which allows users to conveniently run and reproduce the analysis with a script. Comparison to existing methodsHINT implements a new multi-level probabilistic ICA model for group ICA. It provides a statistically principled ICA modeling framework for investigating covariate effects on brain networks. HINT can also generate and visualize model-based network estimates for user-specified subject groups, which greatly facilitates group comparisons. ResultsWe demonstrate the steps and functionality of HINT with an fMRI example data to estimate treatment effects on brain networks while controlling for other covariates. Results demonstrate estimated brain networks and model-based comparisons between the treatment and control groups. In comparisons using synthetic fMRI data, HINT shows desirable statistical power in detecting group differences in networks especially in small sample sizes, while maintaining a low false positive rate. HINT also demonstrates similar or increased accuracy in reconstructing both population- and individual-level source signal maps as compared to some state-of-the-art group ICA methods. ConclusionHINT can provide a useful tool for both statistical and neuroscience researchers to evaluate and test differences in brain networks between subject groups.

Voice Classification Based on Fast Independent Component Analysis to Support Nuclear Power Plant Security

National vital objects have important role in national development,so they require special protection. Nuclear power plant (NPP) is one of them. Access restriction is required to prevent the NPP from potential hazards. The restriction can be improved by using face, fingerprints, retina, iris, and voice password. The improvement will enhance the security of the NPP. This research has implemented pattern recognition and classification of voice passwords. The passwords were a, i, u, e, and o. The features vector was searched by using Fast ICA method while the pattern classification was performed by minimum Euclidean method. The purpose of this research is to recognize and classify those letter password, so people who have access to the nuclear area can be distinguished. The methodology of this research consists of input data, pre-processing data, feature extraction, and classification. Pre-processing was done by normalization, denoising, centering, and whitening. Feature extraction was performed by Fast ICA method, and classification was done by minimum Euclidean distance. The results show that Fast ICA and minimum Euclidean methods can 100 % distinguish between the employees who have access permit and those who have no access permit. When an employee with access permit says aiueo”, it will be recognized as password, whereas when an employee with no access permit says the password, it will be recognized not as the password.

Spectral clustering-based resting-state network detection approach for functional near-infrared spectroscopy.

In recent years, studying the resting-state network (RSN) by using functional near-infrared spectroscopy (fNIRS) has received increased attention. The previous resting-state fNIRS studies mainly adopted the seed-based correlation and the independent component analysis to detect RSN. However, these methods have several inherent problems. For example, the seed-based correlation method relies on seed region selection and neglects the interactions among multiple regions. The ICA method usually relies on manual component selection, which requires rich experience from the experimenter. In the present study, we developed a new approach for fNIRS-RSN detection based on spectral clustering. It consists of two steps. First, it calculates the individual-level partition of the fNIRS measurement region by using spectral clustering with an automatically determined cluster number. Second, the individual-level partitioning results are further clustered. Those clusters with high group consistency are determined as RSN clusters. We validated the method by using simulated data and in vivo fNIRS data. The results showed that the proposed method was effective and robust for fNIRS-RSN detection.

The Application of the ICA Method and Window Dispersion in the Study of Bioequivalence of Drugs

In this paper, the method of estimating bioequivalence, whose main goal is to break the drug concentration curve in the body into components, is considered, implying that this curve is a signal that demonstrates the behavior of the drug. These components are directly related to the main stages of the drug. Denoting the boundaries of these stages, we can, with a minimum of error, compare drugs by the duration and nature of these stages. To isolate the components, methods such as the method of independent components, the window dispersion method, and the study of the variance gamma process will be used.

Rapid Defect Detection for Spacecraft in Infrared Reconstructed Images Using Image Mosaic Technique

In order to satisfy the real-time and large scale requirement of spacecraft nondestructive testing using optical pulsed thermography, in this research, we propose a novel approach based on image mosaic technique which can create large scale or panoramic image mosaics from a set of ordered infrared reconstruction images with overlapping areas for defect detection. First, infrared reconstruction images are extracted from the original thermal video stream by the ICA method based on the idea of blind source signal processing. Then for this research’s special mosaic object, a fast and accurate registration scheme is proposed. Abundant scale rotation invariant feature points are quickly obtained by using the hybrid feature detector and descriptor. Moreover, the feature matching process is realized by applying two-way FLANN and MSAC, parameters of the geometric transformation matrix are estimated by MSAC, and image mosaic is realized according to the geometric transformation model. The experimental results convinced the validity and efficiency of the proposed method.

The Suppression of Powerline Noise for TEM with Coded Source Based on Independent Component Analysis

Powerline noise is one of the most common contaminating types of the observed transient electromagnetic signal. For the conventional TEM method using the bipolar square wave as transmitter waveform, synchronous sampling is the main technology for powerline noise suppression, and notching filtering is sometimes used also. When the transmitter wave has been encoded based on pseudo-random binary sequence, it has proven difficult to achieve the effect by using the above-mentioned conventional methods for the suppression of powerline noise. This is due to the fact that the duration of each logic states of the pseudo-coded transmitter waveform is normally inconsistent. In this study, a method for the suppression of powerline noise is proposed, which is based on the independent component analysis method (ICA). In order to introduce the observation and processing details of this method more clearly, we attempt to apply this method to the time-domain electromagnetic method with coded source researched and developed by the institute of geology and geophysics of Chinese academy of sciences on the basis of the MTEM method. In terms of specific processes, the electrical measurements need to be simultaneously observed in the inline and crossline directions firstly, and then the data will be input into the processing procedures based on ICA method to realize the effective separation of the powerline noise and the useful signals. The processing results of the simulation data and field data show that the method proposed in this paper can suppress the powerline noise effectively, and the processing results build a good data foundation for the subsequent processing.

ICA Model o f Densities f or Images ( ICA DGGDD

In data analysis we use ICA is a basic tool, the aim is that find out a co-ordinate system where are the components of the data are independent.Mostly ICA method such as fastICA and Jointapproximation and diogonalization of eigen matrix (JADE), uses kurtosis as a metric of non gaussianity. But the assumption of kurtosis (fourth order cumulant) may not always satisfies practically. So there are one possible solution is to use skewness (third order cumulant) instead of kurtosis. In this paper we are going to introduce ICA based method, that approach is good for heavy-tailed (fourth order kurtosis) as well as asymmetric data (third order skewness).

Continuous Distant Measurement of the User's Heart Rate in Human-Computer Interaction Applications.

In real world scenarios, the task of estimating heart rate (HR) using video plethysmography (VPG) methods is difficult because many factors could contaminate the pulse signal (i.e., a subjects’ movement, illumination changes). This article presents the evaluation of a VPG system designed for continuous monitoring of the user’s heart rate during typical human-computer interaction scenarios. The impact of human activities while working at the computer (i.e., reading and writing text, playing a game) on the accuracy of HR VPG measurements was examined. Three commonly used signal extraction methods were evaluated: green (G), green-red difference (GRD), blind source separation (ICA). A new method based on an excess green (ExG) image representation was proposed. Three algorithms for estimating pulse rate were used: power spectral density (PSD), autoregressive modeling (AR) and time domain analysis (TIME). In summary, depending on the scenario being studied, different combinations of signal extraction methods and the pulse estimation algorithm ensure optimal heart rate detection results. The best results were obtained for the ICA method: average RMSE = 6.1 bpm (beats per minute). The proposed ExG signal representation outperforms other methods except ICA (RMSE = 11.2 bpm compared to 14.4 bpm for G and 13.0 bmp for GRD). ExG also is the best method in terms of proposed success rate metric (sRate).

Solar Fed Ultra Lift Boost Converter for Induction Motor Drive Applications

Solar energy is one of the major contributors of electrical energy among renewable energy resources. But the low efficiency of solar PV output limits its applications in domestic and industrial field. Suitable MPPT techniques can be used to improve the efficiency of the solar PV cell and the low output voltage of the PV cell can be increased using boost converters. An Ultra lift boost converter is a voltage lift technique based converter, which lifts the input voltage in step by step manner to get high gain output voltage. Compared with conventional non isolated boost converter the voltage lift topology have high efficiency, high gain, low output voltage ripple and high power density. Maximum power from solar panel can be obtained using ICA method. The IMD based industrial and domestic applications are cost effective. The v/f method speed control is most common in case of IMD. In this paper a solar fed Ultra lift boost converter is designed for 3 phase, 1hp IMD. Speed of the induction motor is controlled by closed loop v/f control. For solar output voltage of 100V the results are verified by MATLAB simulink model.

Designing Anti-Jamming Receivers for NR-DCSK Systems Utilizing ICA, WPD, and VMD Methods

In this brief, we consider an advanced noise reduction differential chaotic shift keying system in which a single antenna source communicates with a single antenna destination under the attack of a single antenna jammer. We design an anti-jamming receiver for the considered system. Particularly, we propose a variational mode decomposition—independent component analysis—wavelet packet decomposition (VMD-ICA-WPD) structure, in which VMD method is first exploited to generate multiple signals from the single received one. Second, ICA method is applied to coarsely separate chaotic and jamming signals. After that, WPD method is used to finely estimate and mitigate jamming signals that exist on all outputs of ICA method. Finally, an inverse ICA procedure is carried out, followed by a summation, and the outcome is passed through the conventional correlation receiver for recovering the transmitted information. Simulation results show that the proposed receiver provides significant system performance enhancement compared to that given by the conventional counterparts.

Comparison of brain effective connectivity in different states of attention and consciousness based on EEG signals

Evidence from recent studies on attention and consciousness has raised many questions about their inherent natures and the existence of a possible relationship between them. The main goal of this study is to investigate the significant difference among different states of attention and consciousness, through the connectivity method. These two cognitive phenomena have been studied in recent years with different approaches, but none of them has employed connectivity methods to study the attention and consciousness relationship. Therefore, the connectivity approach is a novel effort in this field. The 8-channel Electroencephalogram signal (EEGs) was used from 45 participants performing a psychophysical visual task. To track the variation of connectivity during time, the EEG recorded data were organized in a particular way, providing 9 1 s signals, including four stimuli classes: attention-consciousness class, attention-unconsciousness class, inattention-consciousness class, and inattention-unconsciousness class. In order to reduce the volume conduction effect, source signals were extracted with the ICA method. The effective connectivity network was computed using the Granger causality method. For the statistical analysis, connectivity indices were presented. This approach suggests that attention-consciousness and attention-unconsciousness classes, and attention-unconsciousness and inattention-unconsciousness classes are the most and least distinctive combinations, respectively. In a more general view provided by averaging the connectivity matrices, all the presented connectivity indices showed significant differences with p-value<0.05 for two combinations: 1-attention-consciousness and attention-unconsciousness classes, and 2- inattention-consciousness and inattention-unconsciousness classes.

Experimental Verification of Modal Identification of a High-rise Building Using Independent Component Analysis

Independent component analysis is one of the linear transformation methods based the techniques for separating blind sources from the output signals of the system. Recently, the method has been analytically applied to the identification of mode shapes and modal responses from the output signal of structures. This study aims to experimentally validate the blind source separation using ICA method and propose a novel method for identification of the modal parameters from the decomposed modal responses. The result of the experimental testing on the three-story steel scale model shows that the mode shapes obtained by ICA method are in good agreement with those by the analytical and peak-picking method in the frequency domain. Based on the robust mathematical model, ICA can calculate the natural frequency and damping ratio effectively using the probability distribution function of the instantaneous natural frequency determined by Hilbert transform of the decomposed modal responses and the change in the output covariance. Finally, the validity of the proposed method paves the way for more effective output-only modal identification for assessment of existing steel-concrete buildings.

Intrinsic Prior Knowledge Driven CICA fMRI Data Analysis for Emotion Recognition Classification

The purpose of this paper is to solve an urgent problem about the selection of differential features under different emotional states. The fMRI data of positive and negative emotional (PNE) states were obtained through strict experimental design, and an improved GICA-IR method was proposed to extract the intrinsic prior information implied in fMRI data and decompose the corresponding functional independent component (IC) in the two states by integrating intrinsic prior information into constraint independent component analysis (CICA) method. The IC-fingerprints corresponding to the functional independent components of the above two states were also calculated, and then they were regarded as the classification features to accurately identify and classify PNE states by the ways of random sub-sampling and support vector machine. The experimental results showed that the presented method had a commendable effect on the classification of positive and negative emotions with an average classification rate of 99%. The proposed improved GICA-IR method was superior to the classical ICA method in terms of the source signal recovery from two aspects of spatial spectrum and power spectrum density, which provided a reliable basis for the good performance of the proposed method based on prior information IC-fingerprint in the emotional classification.