Independent Component Analysis Algorithm Research Articles

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

In the present contribution, a new approach based on mutual information (MI) is proposed for exploring the independence of feasible solutions in two component systems. Investigating how independent are different feasible solutions can be a way to bridge the gap between independent component analysis (ICA) and multivariate curve resolution (MCR) approaches and, to the best of our knowledge, has not been investigated before. For this purpose, different chromatographic and hyperspectral imaging (HSI) datasets were simulated, considering different noise levels and different degrees of overlap for two-component systems. Feasible solutions were then calculated by both grid search (GS) and Lawton-Sylvester (LS) plots. MI map which is the plot of MI vs. rotation matrix elements was used to estimate the degree of independence between different solutions. Inspection of the results showed that the different solutions in the feasible bands correspond to different MI values and that those values are lower for spectral profiles (more independent) than for concentration profiles (more dependent) as expected from the duality concept and the opposite is true. In addition, component profiles are found near more dependent solutions for concentration profiles and near less dependent solutions for spectral profiles which is due to the fact that “independence” constraint was applied to the spectral profiles in ICA algorithms. The performance of three well-known ICA algorithms (mean-field independent component analysis (MF-ICA), mutual information-based least dependent component analysis (MILCA) and joint approximate diagonalization of eigenmatrices (JADE)) as well as MCR-alternating least squares (MCR-ALS) were investigated. MI maps showed that the solutions of MF-ICA and MCR-ALS are in the feasible bands but the MILCA and JADE solutions which are just based on the independence maximization are outside the MI maps.

Cloud based multivariate signal based heart abnormality detection

This paper discusses the rate of heartbeat monitoring with the help of face tracking, extraction of forehead region and separation of blind source. Separation of blind source is applied for three RGB color channel. Independent Component Analysis (ICA) is a powerful tool for such acquisitions. There are mainly four ICA algorithms and these algorithms have been described in the literature. In this paper contribution of two main common ICA algorithms has been studied. These methods are compared to each other in terms of their ability to obtain independent signal from standard RGB signal of forehead region. These methods are Joint Approximate Diagonalization of Eigen matrices (JADE) and Fixed-point ICA (FastICA). Same RGB data set have been applied to these common ICA algorithms and compared with the results of blood volume pulse (BVP) sensor readings. Both methods provide equally consistent results. However, FastICA has shown better results for heart rate measurement compared to JADE.

Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills.

The Electroencephalography (EEG)-based motor imagery (MI) paradigm is one of the most studied technologies for Brain-Computer Interface (BCI) development. Still, the low Signal-to-Noise Ratio (SNR) poses a challenge when constructing EEG-based BCI systems. Moreover, the non-stationary and nonlinear signal issues, the low-spatial data resolution, and the inter- and intra-subject variability hamper the extraction of discriminant features. Indeed, subjects with poor motor skills have difficulties in practicing MI tasks against low SNR scenarios. Here, we propose a subject-dependent preprocessing approach that includes the well-known Surface Laplacian Filtering and Independent Component Analysis algorithms to remove signal artifacts based on the MI performance. In addition, power- and phase-based functional connectivity measures are studied to extract relevant and interpretable patterns and identify subjects of inefficency. As a result, our proposal, Subject-dependent Artifact Removal (SD-AR), improves the MI classification performance in subjects with poor motor skills. Consequently, electrooculography and volume-conduction EEG artifacts are mitigated within a functional connectivity feature-extraction strategy, which favors the classification performance of a straightforward linear classifier.

Prediction of knee trajectory based on surface electromyogram with independent component analysis combined with support vector regression

In recent years, surface electromyogram signals have been increasingly used to operate wearable devices. These devices can aid to help workers or soldiers to lower the load in the task to boost efficiency. However, achieving effective signal prediction has always been a challenge. It is critical to use an appropriate signal preprocessing method and prediction algorithm when developing a controller that can accurately predict and control human movements in real time. For this purpose, this article investigates the effect of various surface electromyogram preprocessing methods and algorithms on prediction results. Walking data (surface electromyogram angle) were collected from 10 adults (5 males and 5 females). To investigate the effect of preprocessing methods on the experimental results, the raw surface electromyogram signals were grouped and subjected to different preprocessing (bandpass/principal component analysis/independent component analysis, respectively). The processed data were then imported into the random forest and support vector regression algorithm for training and prediction. Multiple scenarios were combined to compare the results. The independent component analysis-processed data had the best performance in terms of convergence time and prediction accuracy in the support vector regression algorithm. The prediction accuracy of knee motion with this scheme was 94.54% ± 2.98. Notably, the forecast time was halved in comparison to the other combinations. The independent component analysis algorithm’s “blind source separation” feature effectively separates the original surface electromyogram signal and reduces signal noise, hence increasing prediction efficiency. The main contribution of this work is that the method (independent component analysis + support vector regression) has the potency of best prediction of surface electromyogram signal for knee movement. This work is the first step toward myoelectric control of assisted exoskeleton robots through discrete decoding.

Spatial-Construction-Based Abnormality Detection and Localization for Distributed Parameter Systems

A spatial-construction-based fault diagnosis method is proposed to detect and locate the abnormality for unknown distributed parameter systems (DPSs). To accurately locate the abnormality, the continuous spatial basis functions (SBFs) are derived by the proposed spatial construction method from empirical data. Theoretical analysis proves that the B-spline curve is a proper solution to the spatial construction problem. Two new statistics are constructed based on the derived continuous SBFs and the improved independent component analysis algorithm. The abnormality can be timely detected according to the reference signals derived by the central limit theorem and hypothesis testing. With the continuous SBFs, the probability distribution of statistic contribution can be constructed to reveal the actual position of the abnormality. The proposed method can timely detect and locate the abnormality under fewer sensors without the knowledge of PDE and boundary conditions. The internal short circuit experiment on a lithium-ion battery demonstrates the effectiveness and superiority of the proposed method.

Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording.

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.

Comparing the reliability of different ICA algorithms for fMRI analysis.

Independent component analysis (ICA) has been shown to be a powerful blind source separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets. ICA can extract independent spatial maps and their corresponding time courses from fMRI data without a priori specification of time courses. Some popular ICA algorithms such as Infomax or FastICA generate different results after repeated analysis from the same data volume, which is generally acknowledged as a drawback for ICA approaches. The reliability of some ICA algorithms has been explored by methods such as ICASSO and RAICAR (ranking and averaging independent component analysis by reproducibility). However, the exact algorithmic reliability of different ICA algorithms has not been examined and compared with each other. Here, the quality index generated with ICASSO and spatial correlation coefficients were used to examine the reliability of different ICA algorithms. The results demonstrated that Infomax running 10 times with ICASSO could generate consistent independent components from fMRI data sets.

Backward scattering suppression in an underwater LiDAR signal processing based on CEEMDAN-fast ICA algorithm.

A new signal-processing method to realize blind source separation (BSS) in an underwater lidar-radar system based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA) is presented in this paper. The new statistical signal processing approach can recover weak target reflections from strong backward scattering clutters in turbid water, thus greatly improve the ranging accuracy. The proposed method can overcome the common problem of ICA, i.e. the number of observations must be equal to or larger than the number of sources to be separated, therefore multiple independent observations are required, which normally is realized by repeating the measurements in identical circumstances. In the new approach, the observation matrix for ICA is constructed by CEEMDAN from a single measurement. BSS can be performed on a single measurement of the mixed source signals. The CEEMDAN-ICA method avoid the uncertainty induced by the change of measurement circumstances and reduce the errors in ICA algorithm. In addition, the new approach can also improve the detection efficiency because the number of measurement is reduced. The new approach was tested in an underwater lidar-radar system. A mirror and a white Polyvinyl chloride (PVC) plate were used as target, respectively. Without using the CEEMDAN- Fast ICA, the ranging error with the mirror was 12.5 cm at 2 m distance when the attenuation coefficient of the water was 7.1 m-1. After applying the algorithm, under the same experimental conditions, the ranging accuracy was improved to 4.33 cm. For the PVC plate, the ranging errors were 5.01 cm and 21.54 cm at 3.75 attenuation length with and without the algorithm respectively. In both cases, applying this algorithm can significantly improve the ranging accuracy.

Voice Isolation Using Artificial Neural Network

Abstract: The paper reflects the use of Artificial Neural Networks with the help of various machine learning algorithms for voice isolation. In particular, we consider the case of a voice sample recognition by analyzing the speech signals with the help of machine learning algorithms such as artificial neural networks, independent component analysis, activation function. The strategies by which our central nervous network decodes the network stimuli same as artificial neural network will analyze the given speech sample. After first step, a set of machine learning algorithms will be used like independent component analysis algorithm and gradient function algorithm for processing. After the processing, a decision statement will be applied to generate the desired output. Keywords: Artificial Neural Network, Voice Isolation, Fast Independent Component Analysis, Gradient Descent

Fast gradient algorithm with toral decomposition for complex ICA

The article introduces the new gradient descent/ascent algorithm for independent component analysis for complex valued data (cICA). This algorithm uses toral decomposition of skew-Hermitian gradient matrix and very simple and computationally inexpensive method of projection based on Lie structure of optimization landscape. Toral decomposition is the diagonalization of the gradient matrix. In this stage a very fast Schur decomposition was used. The algorithm was tested for complex communication signals and compared to several classes of algorithms as gradient descent, quasi-Newton and complex JADE. The simulations showed very good results in terms of speed of operation and separation quality, as well as the highest versatility of applications in the context of the cost functions used among all the tested algorithms. The specificity of gradient methods characterized by quick response to changes in the signal indicates the practical usage potential of this method in on-line applications.

Compressive independent component analysis: theory and algorithms

Abstract Compressive learning forms the exciting intersection between compressed sensing and statistical learning where one exploits sparsity of the learning model to reduce the memory and/or computational complexity of the algorithms used to solve the learning task. In this paper, we look at the independent component analysis (ICA) model through the compressive learning lens. In particular, we show that solutions to the cumulant-based ICA model have a particular structure that induces a low-dimensional model set that resides in the cumulant tensor space. By showing that a restricted isometry property holds for random cumulants e.g. Gaussian ensembles, we prove the existence of a compressive ICA scheme. Thereafter, we propose two algorithms of the form of an iterative projection gradient and an alternating steepest descent algorithm for compressive ICA, where the order of compression asserted from the restricted isometry property is realized through empirical results. We provide analysis of the CICA algorithms including the effects of finite samples. The effects of compression are characterized by a trade-off between the sketch size and the statistical efficiency of the ICA estimates. By considering synthetic and real datasets, we show the substantial memory gains achieved over well-known ICA algorithms by using one of the proposed CICA algorithms.

Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.

Cuckoo Search-Based Optimization for Cancer Classification: A New Hybrid Approach.

The design of an optimal framework for the prediction of cancer from high-dimensional and imbalanced microarray data is a challenging job in the fields of bioinformatics and machine learning. There are so many techniques for dimensionality reduction, but it is unclear which of these techniques performs best with different classifiers and datasets. This article focused on the independent component analysis (ICA) features (genes) extraction method for Naïve Bayes (NB) classification of microarray data, because ICA perfectly takes out an independent component from the datasets that satisfy the classification criteria of the NB classifier. A novel hybrid method based on a nature-inspired metaheuristic algorithm is proposed in this article for resolving optimization problems of ICA extracted genes. The cuckoo search (CS) algorithm and artificial bee colony (ABC) for finding the best subset of features to increase the performance of ICA for the NB classifier is designed and executed. According to our investigation, the CS-ABC with ICA was implemented for the first time to resolve the dimensionality reduction problem in high-dimensional microarray biomedical datasets. The CS algorithm improved the local search process of the ABC algorithm, and then the hybrid algorithm CS-ABC provided better optimal gene sets that improved the classification accuracy of the NB classifier. The experimental comparison shows that the CS-ABC approach with the ICA algorithm performs a deeper search in the iterative process, which can avoid premature convergence and produce better results compared with the previously published feature selection algorithm for the NB classifier.

GNSS multipath suppression technology based on postcorrelation and independent component analysis.

In urban and other complex environments, satellite navigation signals are vulnerable to multipath interference, especially medium- and short-delay multipath interference, which seriously affects the positioning accuracy. Aiming at the problem that the effect of traditional methods on medium and short multipath suppression is not obvious and it is difficult to accurately estimate the direction of arrival (DOA) when beamforming is used, a multipath suppression scheme based on a postcorrelation and independent component analysis (ICA) algorithm is proposed in this paper. First, the satellite navigation signal on each array element is despread and demodulated by postcorrelation technology to improve the signal-to-noise ratio (SNR), and then the direct signal component is directly extracted by the ICA algorithm to filter the noise while suppressing the multipath interference. Simulation results show that this method can effectively suppress medium and short multipath interference when the SNR of satellite navigation signals is as low as -30 dB, significantly improve the SNR of navigation messages and reduce the bit error rate, which provides a new solution for global navigation satellite system (GNSS) multipath suppression.

Nature-inspired metaheuristics model for gene selection and classification of biomedical microarray data.

Identifying a small subset of informative genes from a gene expression dataset is an important process for sample classification in the fields of bioinformatics and machine learning. In this process, there are two objectives: first, to minimize the number of selected genes, and second, to maximize the classification accuracy of the used classifier. In this paper, a hybrid machine learning framework based on a nature-inspired cuckoo search (CS) algorithm has been proposed to resolve this problem. The proposed framework is obtained by incorporating the cuckoo search (CS) algorithm with an artificial bee colony (ABC) in the exploitation and exploration of the genetic algorithm (GA). These strategies are used to maintain an appropriate balance between the exploitation and exploration phases of the ABC and GA algorithms in the search process. In preprocessing, the independent component analysis (ICA) method extracts the important genes from the dataset. Then, the proposed gene selection algorithms along with the Naive Bayes (NB) classifier and leave-one-out cross-validation (LOOCV) have been applied to find a small set of informative genes that maximize the classification accuracy. To conduct a comprehensive performance study, proposed algorithms have been applied on six benchmark datasets of gene expression. The experimental comparison shows that the proposed framework (ICA and CS-based hybrid algorithm with NB classifier) performs a deeper search in the iterative process, which can avoid premature convergence and produce better results compared to the previously published feature selection algorithm for the NB classifier.

Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System.

The brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals’ features based on transfer learning and ensemble learning theory. In detail, we used the Independent Component Analysis (ICA) algorithm for the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern (CSP) algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study’s findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface.

BIODICA: a computational environment for Independent Component Analysis.

We developed BIODICA, an integrated computational environment for application of Independent Component Analysis (ICA) to bulk and single-cell molecular profiles, interpretation of the results in terms of biological functions and correlation with metadata. The computational core is the novel Python package stabilized-ica which provides interface to several ICA algorithms, a stabilization procedure, meta-analysis and component interpretation tools. BIODICA is equipped with a user-friendly graphical user interface, allowing non-experienced users to perform the ICA-based omics data analysis. The results are provided in interactive ways, thus facilitating communication with biology experts. BIODICA is implemented in Java, Python and JavaScript. The source code is freely available on GitHub under the MIT and the GNU LGPL licenses. BIODICA is supported on all major operating systems. https://sysbio-curie.github.io/biodica-environment/.

Evaluation of decomposition parameters for high-density surface electromyogram using fast independent component analysis algorithm

The surface electromyogram (sEMG) signal reveals the electrical neuromuscular activities and offers theoretical and clinical information. Recently, exploring motoneuron discharge events at the microscopic level through decomposition has been proposed as a promising analysis approach, which can surpass traditional global sEMG-based analysis method in some aspects. However, computational efficiency is an essential issue when discharge events of individual motor unit are decomposed using blind source separation algorithms. Therefore, choosing proper parameters of decomposition algorithms for different research purposes is important. Accordingly, we have systematically investigated the influences between computation time and decomposition performance for fast independent component analysis (FastICA)-based sEMG decomposition algorithm under different value selections of five decomposition parameters, namely the percentage of eliminated channels, extension factor, the number of decomposition iteration loops, the number of maximum inner loops in each iteration and sampling frequency. We employed high-density sEMG signals from 14 intact subjects during muscle contractions of four-digit extension and flexion at different force levels (20% and 50% maximum voluntary contraction) and a public dataset for sEMG decomposition. According to obtained results, we offer four preference suggestions (less computation time, more motor units, higher accuracy and trade-off). Results show that the trade-off values with consideration of decomposition performance and computation time are recommended as 25% of channels with minimal root mean square, extension factor of 4, 200 iteration loop numbers of decomposition, 20 maximum inner loop numbers and 2048 sampling frequency. Overall, this paper provides a guide for researchers to determine proper decomposition parameters for sEMG decomposition-related works.

A supervised independent component analysis algorithm for motion imagery-based brain computer interface

Recognizing the corresponding neural activities of independent components(ICs) obtained by independent component analysis(ICA) is of prime importance to take use of ICA in EEG analysis. There are many methods trying to solve this problem. But most of them combining ICA, a unsupervised method, and recognition of ICs in a separate way. In this paper, we propose a supervised method to extract the independent components corresponding to different motion imagery(MI) activities in the brain. By designing a new optimization objective and solving it, we combine the idea of ICA with principle of MI in an individual algorithm. From the perspective of event-related desynchronization and synchronization (ERD/ERS), specific frequency band power of the motion-related component should be enhanced or suppressed when executing or imaging movement of body. Therefore, the new optimization function extract the components that satisfy both independence and band power maximization for specific motions. Then, we solve this optimization problem based on the fixed-point iteration scheme. In the experimental stages, we show that our methods can extract motion-related independent components without losing independence. Experimental results show that, although basing on the principle of ERD/ERS, our methods’ effectiveness can be verified in the perspective of movement-related potential (MRP). Additionally, by identifying features in the extracted motion-related independent components, we can achieve better motion recognition accuracy. When using the proposed algorithms with different schema, the results yielded significant accuracy imporvements of 6.9%(p < 0.001) and 7.9%(p < 0.01).

Gender Classification of Mixing and De-mixing Speech

Gender classification is growing in popularity due to the variety of fields in which it can be used. It can be employed in various fields, including criminal investigations and security and authentication services. Gender Classifying speech for different speakers is still a demanding and challenging task for recognizing overlapped speech and building a robust prediction model. The paper provides a gender classification system that uses Independent Component Analysis (ICA) and several machine learning algorithms to identify mixing and de-mixing speech signals. ICA is employed to separate the mixed signal into their source signals. The system consists of two stages: the first stage is the mixing and separating process for signals. The second stage involves combining feature extraction and constructing a classification model to determine whether a signal is male or female based on its acoustic attributes. The system will evaluate the efficacy and significance of machine learning algorithms for selecting the optimal method to identify the speaker's gender with the most excellent efficiency and accuracy. Experimentation shows that the best accuracy value for an SVM model with mixing speeches is 87.1 %, and the best accuracy value for a Neural Net and SVM model with de-mixing speeches is 97.8 %.