Mutual information map as a new way for exploring the independence of chemically meaningful solutions in two-component analytical data

Joint approximate diagonalization of eigenmatrices as a high-throughput approach for analysis of hyphenated and comprehensive two-dimensional gas chromatographic data

The major challenge facing NMR spectroscopic mixture analysis is the overlapping of signals and the arising impossibility to easily recover the structures for identification of the individual components and to integrate separated signals for quantification. In this paper, various independent component analysis (ICA) algorithms [mutual information least dependent component analysis (MILCA); stochastic non-negative ICA (SNICA); joint approximate diagonalization of eigenmatrices (JADE); and robust, accurate, direct ICA algorithm (RADICAL)] as well as deconvolution methods [simple-to-use-interactive self-modeling mixture analysis (SIMPLISMA) and multivariate curve resolution-alternating least squares (MCR-ALS)] are applied for simultaneous (1)H NMR spectroscopic determination of organic substances in complex mixtures. Among others, we studied constituents of the following matrices: honey, soft drinks, and liquids used in electronic cigarettes. Good quality spectral resolution of up to eight-component mixtures was achieved (correlation coefficients between resolved and experimental spectra were not less than 0.90). In general, the relative errors in the recovered concentrations were below 12%. SIMPLISMA and MILCA algorithms were found to be preferable for NMR spectra deconvolution and showed similar performance. The proposed method was used for analysis of authentic samples. The resolved ICA concentrations match well with the results of reference gas chromatography-mass spectrometry as well as the MCR-ALS algorithm used for comparison. ICA deconvolution considerably improves the application range of direct NMR spectroscopy for analysis of complex mixtures.

Independent component analysis and multivariate curve resolution to improve spectral interpretation of complex spectroscopic data sets: Application to infrared spectra of marine organic matter aggregates

In commercial cellular networks, like the systems based on direct sequence code division multiple access (DS-CDMA), many types of interferences can appear, starting from multi-user interference inside each sector in a cell to inter-operator interference. Also unintentional jamming can be present due to co-existing systems at the same band, whereas intentional jamming arises mainly in military applications. Independent Component Analysis (ICA) use as an advanced pre-processing tool for blind suppression of interfering signals in direct sequence spread spectrum communication systems utilizing antenna arrays. The role of ICA is to provide an interference-mitigated signal to the conventional detection. Several ICA algorithms exist for performing Blind Source Separation (BSS). ICA has been used to extract interference signals, but very less literature is available on the performance, i.e., how does it behave in communication environment. This needs an evaluation of its performance in communication environment. This paper evaluates the performance of some major ICA algorithms like Bell and Sejnowski's infomax algorithm, Cardoso's joint approximate diagonalization of eigen matrices (JADE) algorithm, Hyvarinen's fixed point algorithm, Pearson-ICA algorithm and Comon's algorithm in a communication blind source separation problem. Independent signals representing sub-Gaussian, Gaussian and mix users(sub-Gaussian, super-Gaussian and Gaussian) are generated and then mixed linearly to simulate communication signals. Separation performance of ICA algorithms measure by performance index.

Performance of blind source separation algorithms for fMRI analysis using a group ICA method.

This paper deals with the detection of non-metallic anti-personnel (AP) land mines using stepped-frequency ground penetrating radar. A class of the so-called Independent Component Analysis (ICA) represents a powerful tool for such a detection. Various ICA algorithms have been introduces in the literature; therefore there is a need to compare these methods. In this contribution, four of the most common ICA methods are studied and compared to each other as regarding their ability to separate the target and clutter signals. These are the extended Infomax, the FastICA, the Joint Approximate Diagonalization of Eigenmatrices (JADE), and the Second Order Blind Identification (SOBI). The four algorithms have been applied to the same data set which has been collected using an SF-GPR. The area under the Receiver Operating Characteristic (ROC) curve has been used to compare the clutter removal efficiency of the different algorithms. All four methods have given approximately consistent results. However both JADE and SOBI methods have shown better performances over Infomax and FastICA.

In commercial cellular networks, like the systems based on direct sequence code division multiple access (DSCDMA), many types of interferences can appear, starting from multi-user interference inside each sector in a cell to interoperator interference. Also unintentional jamming can be present due to co-existing systems at the same band, whereas intentional jamming arises mainly in military applications. Independent Component Analysis (ICA) use as an advanced pre-processing tool for blind suppression of interfering signals in direct sequence spread spectrum communication systems utilizing antenna arrays. The role of ICA is to provide an interference-mitigated signal to the conventional detection. Several ICA algorithms exist for performing Blind Source Separation (BSS). ICA has been used to extract interference signals, but very less literature is available on the performance, that is, how does it behave in communication environment? This needs an evaluation of its performance in communication environment. This chapter evaluates the performance of some major ICA algorithms like Bell and Sejnowski’s infomax algorithm, Cardoso’s Joint Approximate Diagonalization of Eigen matrices (JADE), Pearson-ICA, and Comon’s algorithm in a communication blind source separation problem. Independent signals representing Sub-Gaussian, Super-Gaussian, and mix users, are generated and then mixed linearly to simulate communication signals. Separation performance of ICA algorithms is measured by performance index.

ObjectiveWe aimed to develop a standardized method to investigate the relationship between estimated brain age and regional morphometric features, meeting the criteria for simplicity, generalization, and intuitive interpretability.MethodsWe utilized T1-weighted magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience project (N = 609) and employed a support vector regression method to train a brain age model. The pre-trained brain age model was applied to the dataset of the brain development project (N = 547). Kraskov (KSG) estimator was used to compute the mutual information (MI) value between brain age and regional morphometric features, including gray matter volume (GMV), white matter volume (WMV), cerebrospinal fluid (CSF) volume, and cortical thickness (CT).ResultsAmong four types of brain features, GMV had the highest MI value (8.71), peaking in the pre-central gyrus (0.69). CSF volume was ranked second (7.76), with the highest MI value in the cingulate (0.87). CT was ranked third (6.22), with the highest MI value in superior temporal gyrus (0.53). WMV had the lowest MI value (4.59), with the insula showing the highest MI value (0.53). For brain parenchyma, the volume of the superior frontal gyrus exhibited the highest MI value (0.80).ConclusionThis is the first demonstration that MI value between estimated brain age and morphometric features may serve as a benchmark for assessing the regional contributions to estimated brain age. Our findings highlighted that both GMV and CSF are the key features that determined the estimated brain age, which may add value to existing computational models of brain age.Critical relevance statementMutual information (MI) analysis reveals gray matter volume (GMV) and cerebrospinal fluid (CSF) volume as pivotal in computing individuals’ brain age.Key PointsMutual information (MI) interprets estimated brain age with morphometric features.Gray matter volume in the pre-central gyrus has the highest MI value for estimated brain age.Cerebrospinal fluid volume in the cingulate has the highest MI value.Regarding brain parenchymal volume, the superior frontal gyrus has the highest MI value.The value of mutual information underscores the key brain regions related to brain age.Graphical

One of the still problems in the Digital Signals Processing is the Blind Signal (Source) Separation (BSS). The BSS mean how to recover the original (source) signals from mixed (observed) signals via many sensors. There are many methods are used in the Blind Signal (Source) Separation problems specifically Cocktail Party problem, such as Independent Component Analysis (ICA), which has become most commonly used. Also, In more cases of the BSS problems especially the Cocktail-Party case there are number of challenges as number of mixed signals and the mixture type. In order, to enhance the performance of the ICA there are many studies for this purpose that depend on the optimization mechanisms as genetic algorithm and Particle Swarm Optimization (PSO). The advantages of a Quantum Particle Swarm Optimization (QPSO) are employed to improve the efficiency of the ICA approach using mutual information function as modern technique, which is used in de-mixing of the speech signals. In this work, a new technique is introduced, is QPSO-based ICA by using Mutual Information function as an objective function for the optimizing process. The presented method has been implemented on the real three different speech signals, with 8 KHz frequency. The results was high accuracy in the signals and more efficient in the computations requirements as the time and space which are measured by the evaluation metrics as the signal plotting, SNR, SDR, and Computation Time.

Independent Component Analysis (ICA) has been proven to be appropriate for extracting blood volume pulse(BVP) signal in Imaging Photoplethysmography (IPPG) techniques. However, related researches commonly employed joint approximate diagonalization of eigenmatrices (JADE) algorithm, with seldom further exploration. In this article, we took comprehensive comparison studies on different ICA or blind source separation (BSS) methods in IPPG techniques from aspects of separation effect, waveform of BVP signal, and CPU time. The experimental results indicate that, Second-Order Blind Identification (SOBI) algorithm has a better performance in BVP extraction, compared to JADE and other ICA algorithms. This study might provide a promising IPPG approach for obtaining high-quality BVP signal, which is significant for non-contact physiological measurements.

Chapter 4 - Riemannian optimization in complex-valued ICA

We propose to use precise estimators of mutual information (MI) to find the least dependent components in a linearly mixed signal. On the one hand, this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand, it has the advantage, compared to other implementations of "independent" component analysis (ICA), some of which are based on crude approximations for MI, that the numerical values of the MI can be used for (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output by comparing the pairwise MIs with those of remixed components; and (iii) clustering the output according to the residual interdependencies. For the MI estimator, we use a recently proposed k -nearest-neighbor-based algorithm. For time sequences, we combine this with delay embedding, in order to take into account nontrivial time correlations. After several tests with artificial data, we apply the resulting MILCA (mutual-information-based least dependent component analysis) algorithm to a real-world dataset, the ECG of a pregnant woman.

Fixed-point ICA algorithm for blind separation of complex mixtures containing both circular and noncircular sources

We investigate the application of independent-component analysis (ICA) to remotely sensed hyperspectral image classification. We focus on the performance of two well-known and frequently used ICA algorithms: joint approximate diagonalization of eigenmatrices (JADE) and FastICA; but the proposed method is applicable to other ICA algorithms. The major advantage of using ICA is its ability to classify objects with unknown spectral signatures in an unknown image scene, i.e., unsupervised classification. However, ICA suffers from computational expensiveness, which limits its application to high-dimensional data analysis. In order to make it applicable or reduce the computation time in hyperspectral image classification, a data-preprocessing procedure is employed to reduce the data dimensionality. Instead of using principal-component analysis (PCA), a noise-adjusted principal-components (NAPC) transform is employed for this purpose, which can reorganize the original data with respect to the signal-to-noise ratio, a more appropriate image-ranking criterion than variance in PCA. The experimental results demonstrate that the major principal components from the NAPC transform can better maintain the object information in the original data than those from PCA. As a result, an ICA algorithm can provide better object classification.

Our goal is to develop a novel BCI based on an eye movements system employing EEG signals on-line. Most of the analysis on EEG signals has been performed using ensemble averaging approaches. However,It is suitable to analyze raw EEG signals in signal processing methods for BCI. In order to process raw EEG signals, we used independent component analysis(ICA). However, we do not know which ICA algorithms have good performance. It is important to check which ICA algorithms have good performance to develop BCIs. Previous paper presented extraction rate of saccade-related EEG signals by five ICA algorithms and eight window size. However, three ICA algorithms, the FastICA, the NG-FICA and the JADE algorithms, are based on 4th order statistic and AMUSE algorithm has an improved algorithm named SOBI.Therefore, we must re-select ICA algorithms. In this paper, we add new algorithms; the SOBI and the MILCA. The SOBI is an improved algorithm based on the AMUSE and uses at least two covariance matrices at different time steps. The MILCA use the independency based on mutual information. Using the Fast ICA, the JADE, the AMUSE, the SOBI, and the MILCA, we extract saccade-related EEG signals and check extracting rates. Secondly, in order to get more robustness against EOG noise, we use improved FastICA with reference signals and check extracting rates.