FastICA Algorithm Research Articles

Real-time isometric finger extension force estimation based on motor unit discharge information

Objective. The goal of this study was to perform real-time estimation of isometric finger extension force using the discharge information of motor units (MUs). Approach. A real-time electromyogram (EMG) decomposition method based on the fast independent component analysis (FastICA) algorithm was developed to extract MU discharge events from high-density (HD) EMG recordings. The decomposition was first performed offline during an initialization period, and the obtained separation matrix was then applied to new data samples in real-time. Since MU pool discharge probability reflects the neural drive to spinal motoneurons, individual finger forces were estimated based on a firing rate-force model established during the initialization, termed the neural-drive method. The conventional EMG amplitude-based method was used to estimate the forces as a comparison, termed the EMG-amplitude method. Simulated HD-EMG signals were first used to evaluate the accuracy of the real-time decomposition. Experimental EMG recordings of 5 min of isometric finger extension with pseudorandom force levels were used to assess the performance of force estimation over time. Main results. The simulation results showed that the accuracy of real-time decomposition was 86%, compared with an offline accuracy of 94%. However, the real-time decomposition accuracy was stable over time. The experimental results showed that the neural-drive method had a significantly smaller root mean square error (RMSE) of the force estimation compared with the EMG-amplitude method, which was consistent across fingers. Additionally, the RMSE of the neural-drive method was stable until 230 s, while the RMSE of the EMG-amplitude method increased progressively over time. Significance. The neural-drive method on real-time finger force estimation was more accurate over time compared with the conventional EMG-amplitude method during prolonged muscle contractions. The outcomes can potentially offer a more accurate and robust neural interface technique for reliable neural-machine interactions based on MU pool discharge information.

Parameter Estimation of Multi-Frequency Hopping Signals Based on FastICA Algorithm

In order to solve the parameter estimation problem while multi-frequency hopping signal aliasing, this paper proposes a multi-frequency hopping parameter estimation algorithm based on FastICA (Fast Independent Component Analysis). Firstly, the FastICA algorithm is used to blindly separate the observed signals, and then using the short-time Fourier transform to extract the time-frequency ridges from separated signals. After that, wavelet transform is used to detect its singularities, and to estimate the period of each frequency-hopping signal. The simulation results show that the proposed algorithm can blindly separate multiple hopping signals and estimate their hopping periods, with fast convergence speed and strong robustness.

Estimation of spreading sequences in LC-DS-CDMA signals based on sparse auto-encoder

A method based on a sparse auto-encoder (SAE) network for the estimation of spreading sequences in long-code direct-sequence code-division multiple access (LC-DS-CDMA) signals is proposed. First, a network classification model based on SAE and softmax classifier is established. Next, the effectiveness of the proposed method is verified by estimating Walsh sequences and m sequences. To estimate the spreading sequences, the LC-DS-CDMA signal is divided into fragments. Then, each user’s spreading sequence is separated by the fast independent component analysis (Fast-ICA) algorithm, and the amplitude fuzziness is eliminated by the delay-and-multiply method. Finally, the spreading sequences are estimated by the SAE model. Experimental results showed that the proposed algorithm could effectively estimate the spreading sequences of LC-DS-CDMA signals. Compared to the existing matching algorithm and Fast-ICA algorithm, the estimation time required by the proposed algorithm was shorter, and its estimation performance at low signal-to-noise ratios was superior.

Radar emitter intrapulse signal blind sorting under modified wavelet denoising

With the electromagnetic environment becoming more and more complex and the analysis demand of the radar emitter intropulse signal presenting more and more urgent, a modified method of the radar emitter intrapulse signal blind sorting under wavelet denoising is proposed. This study aims to improve the weak adaptability to the noise of the fast independent component analysis (FastICA) algorithm and its blind source separating performance. In this method, a pre-processing of noise based on the modified wavelet denoising is added. Then the FastICA algorithm is used to sort the unknown radar emitter intrapulse signal for the next intrapulse signal analysis. Simulations and analysis indicate that the modified method improves the signal to noise ratio of the received intermediate signals and the blind sorting performance.

Fault-Relevant Optimal Ensemble ICA Model for Non-Gaussian Process Monitoring

Independent component analysis (ICA) has been extensively used in non-Gaussian process monitoring. Most ICA-based monitoring methods usually establish a single monitoring model based on a certain dimension reduction criteria. However, the fault information is complex and changeable in actual industrial processes, causing the weak generalization ability of the single monitoring model. From the perspective of ensemble learning, we use the normal and available fault samples to establish several base models and ensemble them to improve the generalization ability of the monitoring model. First, the fastICA algorithm is used to extract all independent components based on the normal data. Then, a multi-objective optimization (NSGA-II) is adopted to optimize the two conditions (accuracy and diversity) of ensemble learning techniques to obtain an optimal set of base models. Finally, the monitoring results of the base models are fused through Bayesian inference. The proposed method can significantly improve the monitoring performance and the generalization ability for process monitoring. Case studies on the TE benchmark process and the wastewater treatment plant illustrate the validity and benefits of the proposed approach. In addition, we also analyzed the effects on the generalization ability of the ensemble model when the number of faults available is different.

FastICA and total variation algorithm for geochemical anomaly extraction

Because the ore-forming system in the crust of the Earth is a highly nonlinear system, geochemical anomaly classification is very important for improving the accuracy of metallogenic prediction. Further, since the distribution of the geochemical elements usually presents nonlinear characteristics due to the complexity and uncertainty of geology factors, the traditional linear data processing method has limited applications for an ore-forming system. The FastICA algorithm is applied to preprocessed geochemical data to reduce the interference information between elements. On the basis of obtaining the separated geochemical elements, the continuity of the spatial distribution of geochemical elements is considered, and combined with the application of the total variation (TV) in image processing; thus the total variation is introduced when processing geochemical data for anomaly analysis to eliminate the influence of singular geochemical data values. To measure the spatial distribution of geochemical elements, assays of the 1:10000 soil geochemical data in the area of Dachaidan in the Qinghai province of China are processed. The elemental anomaly zoning sequences are divided into three levels of anomaly: 85%, 90% and 95%. The anomaly isograms of Au and Cu processed by FastICA and the total variation algorithm predict the geological background of the study area better than the traditional cumulative frequency method. These results indicate that the application of the FastICA algorithm and the total variation algorithm to process geochemical data processing is valid and effective.

An improved FastICA algorithm based on kurtosis index

An improved FastICA algorithm based on kurtosis index

A photoacoustic spectroscopy measurement of near-infrared low-mass multi-component gas at room temperature and atmospheric pressure

Under normal temperature and pressure, intensive overlaps of measured gas absorption lines due to pressure broadening in near infrared (NIR) band lead to a puzzle of the resolution of the measured signal of multi-component photoacoustic spectroscopy (PAS) in a trace gas detection. In this paper, super-Gauss distribution characteristics of NIR and low-mass spectrum at room temperature and pressure are analyzed and demonstrated. A multiple gas PAS sample with the super-Gauss distribution can be regarded as a sum of feature absorptions weighted by the spacing between adjacent feature wavelengths. Several expected feature gases with super-Gauss distribution are simultaneously extracted out from the experimental PAS samples by FastICA algorithm, which was proposed in our former research (‘Feature extraction for gas photoacoustic spectroscopy and content inverse based on overcomplete ICA bases’ published in Optics & Laser Technology). The FastICA algorithm is re-proposed and re-tested within a very limited scanning PAS band. With no exception, the theoretical proof and analyzed cases as well as the test result accord with the gas information of many components from actual overcomplete-ICA basis PAS sample and with super-Gauss distribution which can be extracted in a very limited spectrum (e.g., 0.15 nm in this study) in room temperature and pressure.

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.

Parameter identification of subsynchronous oscillation based on FastICA and MP algorithms

Parameter identification of subsynchronous oscillation based on FastICA and MP algorithms

ECG Signal Enhancement Method Based on Independent Component Analysis and Its Application

In order to enhance the ECG signal, this paper uses independent component analysis to process the mixed noise output signal. Firstly, a simulator based on Fourier series is used to simulate an ECG signal and the noise signal of power frequency signal is replaced by sinusoidal signal, and the two signals are randomly mixed. Then the mixed signals are separated by FastICA algorithm, and the ideal separation results are obtained. The results show that the correlation coefficient between the separated ECG signal and the original ECG signal is 0.9659, which indicates that the FastICA algorithm is feasible for medical signal enhancement.

FICA: FastICA Algorithms and Their Improved Variants

fICA: FastICA Algorithms and Their Improved Variants

Degenerate Unmixing Estimation Technique (DUET) for Fetal ECG Blind Source Separation

Monitoring the health of fetus at early stage is very crucial. One of the non-invasive ways is by evaluating the pattern of electrocardiogram (ECG) signals of mother’s abdomen and thorax. As these raw signals are mixed signals that consist of mainly maternal ECG (MECG) and fetal ECG (FECG), an effective extraction method of FECG from the mixed signals is imperative. Fast Independent Component Analysis (FastICA) is one of the common signal processing algorithms for blind source separation (BSS). However, it works only for even-determined case when the number of sources (MECG and FECG) is equal to the number of mixtures (two mixed signals i.e. from the mother’s abdomen and the mother’s thorax). For the underdetermined case in which the number of sources would be more than the number of mixture (for the case of twins or triplets’ pregnancy), FastICA algorithm fails as the computation of the inverse mixing matrix of ICA is theoritically impossible. Thus, Degenerate Unmixing Estimation Technique (DUET) algorithm which is based on signal recovery sparsity algorithm is implemented in this research so as to discover its feasibility to solve both even-determined and underdetermined cases. From the experimental results, the DUET performance is promising. Although for even-determined case, FastICA is better than DUET performance, DUET is proved to be valuable in solving the underdetermined case problem, yet the low FECG signal to noise ratio (SNR) value is observed reflecting the high interference.

Research of rectal dynamic function diagnosis based on FastICA‐STFT

Aiming at the lack of signal preprocessing in current rectal signal research, a new 15th-order Newton iterative fast independent component analysis (FastICA) algorithm is proposed to separate the signal. To solve the incomplete problem of the single channel rectal pressure signal, C-C phase space reconstruction technology is used for multi-dimensional signal reconstruction. In order to achieve the diagnosis of rectal dynamic after artificial anal sphincter implantation, the preprocessed rectal signal component is subjected to short-time Fourier transforms for time–frequency analysis. This constitutes a complete rectal dynamic function diagnosis process. The non-stationary simulation signal is constructed to verify the preprocessing method. A complete diagnostic experiment is performed on two rectal signals of different gut dynamic. Simulation results show that the proposed method can realise the preprocessing of non-stationary signals. Through the rectal dynamic function experiment based FastICA-STFT, two different rectal signals are distinguished, and the results are consistent with the clinical diagnosis, which has achieved the diagnosis of rectal motility. This study will provide support for the recovery of rectal dynamic function in patients with artificial anal sphincter implants.

Research on Applications of FastICA Algorithm in the Detection of Dangerous Liquids

In the actual environment of security detection, many kinds of liquids often exist in the same detection background, and their dangerous levels are difficult to identify. Therefore, it is very important to research on identifying the dangerous levels of various liquids. The paper establishes the [Formula: see text]-parameter database of tested samples under specific detection environment with free space method. In the actual detection, ultra-wide-band (UWB) centimeter wave is used to measure the [Formula: see text]-parameters of several detected liquids first. Then the fast independent component analysis (FastICA) algorithm is used for unmixing the mixed signal by Newton’s iteration method and the negative entropy maximization search principle. The unmixed signal matches with the sample database adaptively, so the dangerous levels of the detected liquids are identified. Multiple experiments show that FastICA algorithm can reach a matching rate of 95% between water and 90[Formula: see text] gasoline or alcohol and 90[Formula: see text] gasoline, it also can reach a matching rate of around 73% between water and alcohol. This algorithm has a quick response and high reliability for identification of dangerous liquids. FastICA algorithm in this paper is applied for detecting the dangerous liquids for the first time, and it has high application value.

No-reference image quality assessment based on quality patches in real time

Image quality assessment methods quantify the quality of an image that is highly correlated with human-perceived image quality. In this paper, the image quality is represented by the image patches that are selected by applying the spatial correlation of pixel-pairs. The quality image patches are selected from the database LIVE2, and quality feature detectors are learned by running the FastICA algorithm on the patches. Then being quality features, the independent component coefficients are found out from each quality image patch. A Hash lookup table is built by the binarization of the independent component coefficients of quality image patches, and the Hash table can be indexed by the binary code of the independent component coefficients of a tested image. The results of proposed approach were compared with results from other methods of image quality assessment and with the subjective image quality evaluated scores. And the experimental results expressed that the proposed metric method of no-reference image quality assessment could accurately measure the distorted degree of images. The Pearson linear correlation coefficient (PCC) achieves a high value 0.949 for the accuracy of evaluating results. The Spearman order correlation (SRC) achieves a high value 0.996 for the monotonicity of evaluating results. And the root mean square error (RMSE) is 5.917 for the subjective consistency of evaluating results. Extra evaluating aerial images, the proposed method obtained the result that the foggy weather led to the worst quality.

Comparison study of fast independent component analysis and constrained independent component analysis

Constrained Independent Component Analysis (cICA) was developed from Independent Component Analysis (ICA), and the concepts and principles of independent component analysis still apply to constrained independent component analysis. This paper introduces the mathematical basis, basic principles and algorithms of constrained independent component analysis. Firstly, the mathematical basis of cICA is introduced. It mainly includes the entropy and negative entropy of irrelevant and whitening, Statistical opposition and multivariate Gaussian density random variables. Then the mathematical model of Constrained FastICA, the identifiability and uncertainty of the model, the FastICA independence criterion, FastICA optimization and preprocessing, FastICA algorithm and simulation examples are introduced. Finally, the shortcomings of fast independent component analysis and the advantages of constrained independent component analysis are analyzed. According to the establishment and evaluation indicators of signals, the basic principles and corresponding algorithms of constrained independent component analysis are introduced. The characteristics of the corresponding simulation show that the algorithm is effective in extracting signal characteristics.

Fast high-order matched filter for hyperspectral image target detection

Hyperspectral image target detection is an important application for both of its civil and military uses. Traditional hyperspectral image target detection algorithms are usually designed based on the second-order statistics of the data where it is assumed that the target follows a Gaussian distribution. However, due to the low spatial resolution of the hyperspectral sensor, sometimes the targets of interest only occupy a few pixels. In this case, targets are more suitable to be described by high-order statistics. In this paper, we propose a fast high-order matched filter (FHMF) algorithm which describes the essence of the data more properly and then formulate the detection problem as an optimization problem. To solve the optimization problem efficiently, a fixed-point algorithm is proposed inspired by the FastICA algorithm of the signal processing field. The experiments are conducted with a synthetic hyperspectral image and a real hyperspectral image. The experiment results show that FHMF has better detection performance than other classical detection algorithms. In addition, the fast convergence speed also demonstrates the effectiveness of the proposed fixed-point algorithm.

Clustering via finite nonparametric ICA mixture models

We propose an extension of non-parametric multivariate finite mixture models by dropping the standard conditional independence assumption and incorporating the independent component analysis (ICA) structure instead. We formulate an objective function in terms of penalized smoothed Kullback Leibler distance and introduce the nonlinear smoothed majorization-minimization independent component analysis (NSMM-ICA) algorithm for optimizing this function and estimating the model parameters. We have implemented a practical version of this algorithm, which utilizes the FastICA algorithm, in the R package icamix. We illustrate this new methodology using several applications in unsupervised learning and image processing.

HD-sEMG-based research on activation heterogeneity of skeletal muscles and the joint force estimation during elbow flexion

Objective. To investigate the activation heterogeneity of skeletal muscles and realize the joint force estimation during the elbow flexion task. Approach. When an isometric elbow flexion task was performed, high-density surface electromyography (HD-sEMG) signals from a grid covering the front and inside of the upper arm and the generated joint force were recorded synchronously. HD-sEMG signals were preprocessed and then decomposed into source signals corresponding to biceps brachhi (BB) and brachialis (BR) and their contribution vectors using a fast, independent component analysis (FastICA) algorithm. The activation heterogeneity of BB and BR was investigated from the activation level and activation region, initially. Then, the contribution combinations of two sources were classified into several major clusters using the K-means clustering method. Afterwards, input signals for force estimation were extracted from the major clusters corresponding to different combinations, and the polynomial fitting technique was adopted as the force estimation model. Finally, the force estimation results were obtained and the analysis around the force estimation performance using different input signals was conducted. Main results. Ten subjects were recruited in this research. The experimental results demonstrated that it is feasible to analyze the activation heterogeneity of muscles from the activation level and activation region, and to select the appropriate region of the HD-sEMG grid for high performance force estimation. For the isometric elbow flexion task, joint force estimation accuracy could be improved when the input signal was extracted from the specific area where the contribution difference of BB and BR to the HD-sEMG signals were relatively small. Significance. The proposed framework provided a novel way to explore the relationship between muscle activation and the generating joint force, and could be extended to multiple noteworthy research fields such as myoelectric prostheses, sports biomechanics, and muscle disease diagnosis.