Independent Component Analysis Algorithm Research Articles

A SOC Design of ORICA-based Highly Effective Real-time Multi-channel EEG System.

Independent component analysis (ICA) has been wildly used to improve EEG based application such as brain computer interface (BCI). However, some well know ICA algorithm, such as Infomax ICA, suffering from the problem of convergence latency and make it hard to be apply on real-time application. This paper proposes a highly efficient chip implementation of multi-channel EEG real-time system based on online recursive independent component analysis algorithm (ORICA). The core size of the chip is 1.5525-mm2 using 28nm CMOS technology. The EEG demonstration board will be implemented with the ORICA chip. The operation frequency and power consumption of the chip are 100 MHz and 17.9 mW respectively. The proposed chip was validated with a real-time circuit integrated system and the average correlation coefficient between simulations results and chip processing results is 0.958.

Design and implementation of EEMD-assisted ICA joint denoising scheme for ECG signals

Independent Component Analysis (ICA) algorithm is a signal processing method for solving blind source separation(BSS) problem, which can remove noises in observed signals and obtain original signals. This paper designs and implements an EEMD-assisted ICA joint denoising scheme for ECG signals. Firstly, Ensemble Empirical Mode Decomposition (EEMD) is used to perform noise-assisted data analysis on ECG signals, completing pre-denoising of ECG signals and pre-processing for subsequent ICA analysis. Next, to more thoroughly remove noises in ECG signals, ICA separates independent components from pre-denoised signals. Finally, signal reconstruction restores original ECG signals, so as to realize ECG denoising. Experimental results show that the scheme can effectively remove common noises, and get clean ECG signals, which lay a good foundation for accurate diagnosis of heart patients.

PSASL: Pixel-Level and Superpixel-Level Aware Subspace Learning for Hyperspectral Image Classification

The performance of hyperspectral image (HSI) classification relies on the pixel information obtained from hundreds of contiguous and narrow spectral bands. Existing approaches, however, are limited to exploit an appropriate latent subspace for data representation within the pixel-level or superpixel-level. To utilize spectral information and spatial correlation among pixels in HSI and avoid the “salt-and-pepper” problem generated in the pixel-based HSI classification, a novel pixel-level and superpixel-level aware subspace learning method called PSASL is developed. The PSASL constructs the subspace learning framework based on the reconstruction independent component analysis algorithm. The spectral–spatial graph regularization and label space regularization are developed as the pixel-level constraints. To avoid the “salt-and-pepper” problem generated in the pixel-based classification methods, superpixel-level constraints are introduced for integrating the data representations defined in the subspace and class probabilities of the pixels in the same superpixel. The subspace learning and the pixel-level regularization are combined with the superpixel-level regularization to form a unified objective function. The solution to the objective function is efficiently achieved by employing a customized iterative algorithm, and it converges very fast. A discriminative data representation and a universal multiclass classifier are learned simultaneously. We test the PSASL on three widely used HSI data sets. Experimental results demonstrate the superior performance of our method over many recently proposed methods in HSI classification.

To Explore the Potentials of Independent Component Analysis in Brain-Computer Interface of Motor Imagery.

This paper is focused on the experimental approach to explore the potential of independent component analysis (ICA) in the context of motor imagery (MI)-based brain-computer interface (BCI). We presented a simple and efficient algorithmic framework of ICA-based MI BCI (ICA-MIBCI) for the evaluation of four classical ICA algorithms (Infomax, FastICA, Jade, and Sobi) as well as a simplified Infomax (sInfomax). Two novel performance indexes, self-test accuracy and the number of invalid ICA filters, were employed to assess the performance of MIBCI based on different ICA variants. As a reference method, common spatial pattern (CSP), a commonly-used spatial filtering method, was employed for the comparative study between ICA-MIBCI and CSP-MIBCI. The experimental results showed that sInfomax-based spatial filters exhibited significantly better transferability in session to session and subject to subject transfer as compared to CSP-based spatial filters. The online experiment was also introduced to demonstrate the practicability and feasibility of sInfomax-based MIBCI. However, four classical ICA variants, especially FastICA, Jade, and Sobi, performed much worse as compared to sInfomax and CSP in terms of classification accuracy and stability. We consider that conventional ICA-based spatial filtering methods tend to be overfitting while applied to real-life electroencephalogram data. Nevertheless, the sInfomax-based experimental results indicate that ICA methods have a great space for improvement in the application of MIBCI. We believe that this paper could bring forth new ideas for the practical implementation of ICA-MIBCI.

Research of improved fast independent component analysis algorithm in rectal diagnosis signal preprocessing.

With the maturity of artificial organ technology, the use of artificial anal sphincters was proposed to help patients who suffered anal incontinence for various causes reconstruct rectal perception, monitor rectal pressure and diagnose rectal lesion. Aimed at the lack of signal pretreatment in the artificial anal sphincter system, we find a way to solve it, that is, the multi-dimensional reconstruction of the intestinal one-dimensional pressure signal sequence by using phase space reconstruction, and the separation of the reconstructed signal by using the improved fast independent component analysis algorithm. We did some relevant experiments, further extracted the features of the isolated rectal signal, and used back propagation neural network to diagnose the rectal lesions. Experiments show that the method can pretreat the rectal signal, and further analyze the separated signal to diagnose of rectal function. The improved fast independent component analysis algorithm has few iterations, fast convergence, short run time, low requirements on initial weights and good diagnosis. This study lays a foundation for the diagnosis of rectal function by using artificial anal sphincters.

Comparison of Complex-Valued Independent Component Analysis Algorithms for EEG Data

Independent Component Analysis (ICA) has been successfully applied to a variety of problems, from speaker identification and image processing to functional magnetic resonance imaging (fMRI) of the brain. In particular, it has been applied to analyze EEG data in order to estimate the sources form the measurements. However, it soon became clear that for EEG signals the solutions found by ICA often depends on the particular ICA algorithm, and that the solutions may not always have a physiologically plausible interpretation. Therefore, nowadays many researchers are using ICA largely for artifact detection and removal from EEG, but not for the actual analysis of signals from cortical sources. However, a recent modification of an ICA algorithm has been applied successfully to EEG signals from the resting state. The key idea was to perform a particular preprocessing and then apply a complexvalued ICA algorithm. In this paper, we consider multiple complex-valued ICA algorithms and compare their performance on real-world resting state EEG data. Such a comparison is problematic because the way of mixing the original sources (the “ground truth”) is not known. We address this by developing proper measures to compare the results from multiple algorithms. The comparisons consider the ability of an algorithm to find interesting independent sources, i.e. those related to brain activity and not to artifact activity. The performance of locating a dipole for each separated independent component is considered in the comparison as well. Our results suggest that when using complex-valued ICA algorithms on preprocessed signals the resting state EEG activity can be analyzed in terms of physiological properties. This reestablishes the suitability of ICA for EEG analysis beyond the detection and removal of artifacts with real-valued ICA applied to the signals in the time-domain.

Suppression of Impulsive Noise in Power Line Communication using Weighted FastICA

In this paper, a weighted blind source separation (BSS) method is developed to eliminate α -stable distribution impulsive noise in the power line communication (PLC). In this work, a weighted independent component analysis(ICA) is conceived to construct determined ICA model from a single channel observation. This operation can achieve the advantages of low complexity blind separation work and exempting channel parameter estimation. The system noise model is described by the transmission characteristic of power line. Simulation experiments show that the proposed ICA algorithm can effectively achieve noise elimination, and guarantee the practical communication requirements.

Accurate Detection of ECG Signals in ECG Monitoring Systems by Eliminating the Motion Artifacts and Improving the Signal Quality Using SSG Filter with DBE

The automatic recognition of QRS complexes in an Electrocardiography (ECG) signal is a critical step in any programmed ECG signal investigation, particularly when the ECG signal taken from the pregnant women additionally contains the signal of the fetus and some motion artifact signals. Separation of ECG signals of mother and fetus and investigation of the cardiac disorders of the mother are demanding tasks, since only one single device is utilized and it gets a blend of different heart beats. In order to resolve such problems we propose a design of new reconfigurable Subtractive Savitzky–Golay (SSG) filter with Digital Processor Back-end (DBE) in this paper. The separation of signals is done using Independent Component Analysis (ICA) algorithm and then the motion artifacts are removed from the extracted mother’s signal. The combinational use of SSG filter and DBE enhances the signal quality and helps in detecting the QRS complex from the ECG signal particularly the R peak accurately. The experimental results of ECG signal analysis show the importance of our proposed method.

A novel fixed-point algorithm for constrained independent component analysis

Constrained independent component analysis (ICA) is an effective method for solving the blind source separation with a prior knowledge. However, most constrained ICA algorithms are proposed for the real-valued sources. In this paper, a novel constrained noncircular complex fast independent component analysis (c-ncFastICA) algorithm based on the fixed-point learning is proposed to address the complex-valued sources. The c-ncFastICA algorithm uses the augmented Lagrangian method to obtain a new cost function and then utilizes the quasi-Newton method to search its optimal solution. Compared with other ICA and constrained ICA algorithms, c-ncFastICA has better separation performance. Simulations confirm the effectiveness and superiority of the c-ncFastICA algorithm.

Home-based mobile fetal/maternal electrocardiogram acquisition and extraction with cloud assistance.

Electrocardiogram (ECG) monitoring of the fetus during pregnancy, before and during labor, can provide crucial information for the assessment of fetal well-being and development, as well as labor progress. An out-of-clinics fetal ECG monitoring system may pave the way for instant diagnosis, suggesting immediate intervention, which could help reduce the fetal mortality rate. In this paper, we present an unobtrusive fetal maternal ECG monitoring system which can operate in the home setting. The acquisition of the mother's abdominal ECG is done using the non-contact electrode approach. The extraction of the fetal ECG from the combined fetal/maternal ECG signal is investigated using both Fast Independent Component Analysis (FastICA) and RobustICA algorithms. An accelerometer is integrated for motion artifact detection which would help reduce interferences due to movement. The device also is connected to a cloud server, allowing doctors to access the data in real time.

Statistical monitoring for non-Gaussian processes based on MICA-KDR method

The focus of the current work attempts to propose a purely data-based model for generating residuals for non-Gaussian process monitoring purposes, the idea of residual generation is borrowed from the field of model-based fault detection and applied in statistical monitoring, the generated residual instead of the measured variables is thus modeled and monitored. The proposed approach first employs the modified independent component analysis (MICA) algorithm to extract independent components (ICs) from a given dataset. Secondly, through assuming but only one variable is missing at one time, the known data regression (KDR) method dealing with missing data problem is then used for estimating the corresponding ICs. The inconsistency between the actual and estimated ICs is called residual and may present much lower level of non-Gaussianity, in contrast to the actual ICs. Thirdly, a principal component analysis based statistical monitoring model can be utilized for online fault detection based on the generated residual. Finally, the superiority and efficiency of the MICA-KDR approach over its counterparts are validated by implementing comparisons on two industrial processes, the proposed MICA-KDR method is demonstrated to be a comparative alternative in monitoring non-Gaussian processes

Blind Multiple-Input Multiple-Output Image Phase Retrieval

In this paper, we consider the problem of recovering the phase information of the multiple images from the multiple mixed phaseless short-time Fourier transform image measurements, which is called the blind multiple-input multiple-output image phase retrieval (BMIPR) problem. It is an inherently ill-posed problem due to the lack of the phase and mixing information, and the existing phase retrieval algorithms are not explicitly designed for this case. To address the BMIPR phase retrieval problem, an integrated algorithm is presented, which combines a gradient descent algorithm by minimizing a nonconvex loss function with an independent component analysis algorithm and a nonlocal means algorithm. Experimental evaluation has been conducted to show that under appropriate conditions, the proposed algorithms can explicitly recover the images, the phases of the images, and the mixing matrix. In addition, the algorithm is robust to noise.

How many channels are suitable for independent component analysis in motor imagery brain-computer interface

Individual differences of classification performance remain a crucial problem in electroencephalography (EEG)-based motor imagery brain computer interface (MIBCI). Independent component analysis (ICA) is a promising spatial filtering technique in BCI system for it requires few and unlabeled training samples for calibration of the BCI system. However, both the distribution of scalp electrodes and the quality of training data are critical factors of influencing the classification performance of ICA-BCI applications. In this study, a new channel selection algorithm was proposed for automatically choosing subject-specific minimal electrode subsets that can obtain high classification accuracies of the ICA-BCI system. The algorithm consisted of two steps: selection of “main electrodes” located on the motor cortex, and subsequent searching of “subordinate electrodes”, which were picked out one by one from the left electrodes until the maximum accuracy was achieved. Meanwhile, a single_trial_based_self_testing (STST) method, utilizing one single trial to train ICA spatial filters which were only applied in the identical trial for extracting motor-related independent components (MRICs), was proposed to eliminate the influence of bad trials. The channel selection algorithm was applied in 72 runs of three-class motor imagery EEG datasets for twelve BCI users. Experimental results indicated that the classification accuracies using the optimal channels were significantly higher than that of standard 8 and 9 channels. Meanwhile, ICA algorithm with optimal channel subset had comparable performance with Common spatial patterns (CSP) algorithm in self-testing and run-to-run cross validation, and ICA significantly outperformed CSP in session-to-session and subject-to-subject transfer. Although the numbers and locations of optimal channels were different between sessions and subjects, the main electrodes were basically same between different runs for long-term BCI users. Furthermore, the optimal electrodes were primarily located on the motor cortex of parietal lobe area and the frontal lobe area, few located in the occipital lobe area. Too many or too few channels were not suitable for ICA calculation, and usually, using 5–8 channels of EEG data could achieve better classification performance. These findings may offer a reference for the optimization of ICA-based BCI systems, and further improve the performance and stability of MI-BCI system.

A Fetal ECG Monitoring System Based on the Android Smartphone.

In this paper, a fetal electrocardiogram (ECG) monitoring system based on the Android smartphone was proposed. We designed a portable low-power fetal ECG collector, which collected maternal abdominal ECG signals in real time. The ECG data were sent to a smartphone client via Bluetooth. Smartphone app software was developed based on the Android system. The app integrated the fast fixed-point algorithm for independent component analysis (FastICA) and the sample entropy algorithm, for the sake of real-time extraction of fetal ECG signals from the maternal abdominal ECG signals. The fetal heart rate was computed using the extracted fetal ECG signals. Experimental results showed that the FastICA algorithm can extract a clear fetal ECG, and the sample entropy can correctly determine the channel where the fetal ECG is located. The proposed fetal ECG monitoring system may be feasible for non-invasive, real-time monitoring of fetal ECGs.

Multiple Input Single Output Phase Retrieval

In this paper, we consider the problem of recovering the phase information of multiple sources from a mixed phaseless Short-Time Fourier Transform (STFT) measurement, which is called multiple input single output (MISO) phase retrieval problem. It is an inherently ill-posed problem due to the lack of the phase and mixing information, and the existing phase retrieval algorithms are not explicitly designed for this case. To address the MISO phase retrieval problem, a least squares (LS) method coupled with an independent component analysis (ICA) algorithm is proposed for the case of sufficiently long window length. When these conditions are not met, an integrated algorithm is presented, which combines a gradient descent (GD) algorithm by minimizing a non-convex loss function with an ICA algorithm. Experimental evaluation has been conducted to show that under appropriate conditions the proposed algorithms can explicitly recover the signals, the phases of the signals and the mixing matrix. In addition, the algorithm is robust to noise

Performance comparison of MeRMaId-ICA and Np-ICA in composite abdominal electrocardiogram separation

Blind source separation strives to disintegrate a multivariate composite non-invasive abdominal electrocardiogram signal into independent non-Gaussian signals such as maternal and foetal electrocardiograms. This paper proffers two separation algorithms especially for foetal electrocardiogram (FECG) extraction as it has a great role in diagnosing the foetal heart deformities namely minimum Renyi's mutual information called MeRMaId algorithm and non-parametric independent component analysis (Np-ICA) algorithm. Both the algorithms are experimentally evaluated on synthetic and real abdominal data. Performance juxtaposition of these two algorithms is done by scrutinising the signal to noise ratio at assorted noise levels and signal to interference ratio at assorted amplitude levels, and computing correlation coefficient (ρ) of the original and the estimated maternal and foetal electrocardiograms.

Optimization of RLS Algorithm for Hybrid Method ICA-RLS

This paper deals with a design and implementation of a new approach to the extraction of fetal electrocardiography (fECG) signal by hybrid method combining independent component analysis (ICA) and adaptive algorithm based on recursive optimal adaptation (Recursive Least Square, RLS). The abdominal and direct fetal electrocardiogram database (ADFECGDB) was selected for the validation based on estimation of detection accuracy (ACC) relative to the reference signal recorded by means of fetal scalp electrode. The paper provides the tests of the RLS-based filter order setting in the range of 2 to 100 as well as optimization of the input channel combination to achieve the most accurate detection. In total, 11 electrode combinations were created out of 4 abdominal channels, and tested as the inputs of the ICA-RLS hybrid method. The results show that the method is very promising. The highest score was achieved for recording r0l ACC = 99.228 % with combination of input channels 1, 2, and 3 along with the RLS algorithm filter order 4.

Hybrid independent component analysis (H-ICA) with simultaneous analysis of high-order and second-order statistics for industrial process monitoring

Both the Gaussian and non-Gaussian information exist simultaneously in many real industrial processes. However, the standard independent component analysis (ICA) cannot effectively explore process information when there is more than one independent component (IC) following Gaussian distribution. Besides, process may present an obvious temporal structure due to wide applications of controllers, which has not been investigated by standard ICA. To solve the above problem, the paper proposed a hybrid ICA (H-ICA) algorithm for process monitoring by concurrent analysis of both high-order and second-order statistics. The time-delayed correlation matrices are first diagonalized for robust whitening to remove the effect of the additive white noise. Then, non-Gaussian information is explored by FastICA with the utilization of higher-order statistics, and Gaussian information is extracted by analyzing the time structure information contained in second-order nonzero-delayed covariance matrices. By the hybrid analysis of high-order and second-order statistics, both non-Gaussian-distributed and Gaussian-distributed ICs are extracted. In this way, more comprehensive process information is fully investigated and analyzed for the development of process monitoring strategy. The proposed H-ICA process monitoring algorithm is verified by both a numerical example and a real thermal power plant process which illustrates its feasibility and efficacy.

Real‐time diagnosis of high‐speed rail traction transformer in different topologies

High-speed railway (HSR) traction transformer, as a connection between traction power supply system and the power grid, is critical in electrified railway design, which requires a drastic change of traction load and frequent external short circuits. In the actual operation process, the traction transformer can produce minor faults that are difficult to detect such as the deformation of the winding, which may result in a serious problem. This study analyses the topology structures and winding fault characteristics of six HSR traction transformers consisting of single phase, Vv, YNd11, Scott, YNvd and impedance matching wiring. Both circuit models of normal operation and fault operation of HSR traction transformer are established. Results show that real-time diagnosis of HSR traction transformer can be accomplished through measuring the voltage signal of the input and output points during operation and establishing real-time monitoring statistics of small faults standardisation（I 2 and squared prediction error）based on fast independent component analysis algorithm, as well as the signal statistics distribution, thereby improving the safety and reliability of HSR operation.

Detecting neural assemblies in calcium imaging data

BackgroundActivity in populations of neurons often takes the form of assemblies, where specific groups of neurons tend to activate at the same time. However, in calcium imaging data, reliably identifying these assemblies is a challenging problem, and the relative performance of different assembly-detection algorithms is unknown.ResultsTo test the performance of several recently proposed assembly-detection algorithms, we first generated large surrogate datasets of calcium imaging data with predefined assembly structures and characterised the ability of the algorithms to recover known assemblies. The algorithms we tested are based on independent component analysis (ICA), principal component analysis (Promax), similarity analysis (CORE), singular value decomposition (SVD), graph theory (SGC), and frequent item set mining (FIM-X). When applied to the simulated data and tested against parameters such as array size, number of assemblies, assembly size and overlap, and signal strength, the SGC and ICA algorithms and a modified form of the Promax algorithm performed well, while PCA-Promax and FIM-X did less well, for instance, showing a strong dependence on the size of the neural array. Notably, we identified additional analyses that can improve their importance. Next, we applied the same algorithms to a dataset of activity in the zebrafish optic tectum evoked by simple visual stimuli, and found that the SGC algorithm recovered assemblies closest to the averaged responses.ConclusionsOur findings suggest that the neural assemblies recovered from calcium imaging data can vary considerably with the choice of algorithm, but that some algorithms reliably perform better than others. This suggests that previous results using these algorithms may need to be reevaluated in this light.