Adaptive Independent Component Analysis Research Articles

Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor.

Spike extraction by blind source separation (BSS) algorithms can successfully extract physiologically meaningful information from the sEMG signal, as they are able to identify motor unit (MU) discharges involved in muscle contractions. However, BSS approaches are currently restricted to isometric contractions, limiting their applicability in real-world scenarios. We present a strategy to track MUs across different dynamic hand gestures using adaptive independent component analysis (ICA): first, a pool of MUs is identified during isometric contractions, and the decomposition parameters are stored; during dynamic gestures, the decomposition parameters are updated online in an unsupervised fashion, yielding the refined MUs; then, a Pan-Tompkins-inspired algorithm detects the spikes in each MUs; finally, the identified spikes are fed to a classifier to recognize the gesture. We validate our approach on a 4-subject, 7-gesture + rest dataset collected with our custom 16-channel dry sEMG armband, achieving an average balanced accuracy of 85.58±14.91% and macro-F1 score of 85.86±14.48%. We deploy our solution onto GAP9, a parallel ultra-low-power microcontroller specialized for computation-intensive linear algebra applications at the edge, obtaining an energy consumption of 4.72 mJ @ 240 MHz and a latency of 121.3 ms for each 200 ms-long window of sEMG signal.

Single-channel EEG signal extraction based on DWT, CEEMDAN, and ICA method.

In special application scenarios, such as portable anesthesia depth monitoring, portable emotional state recognition and portable sleep monitoring, electroencephalogram (EEG) signal acquisition equipment is required to be convenient and easy to use. It is difficult to remove electrooculogram (EOG) artifacts when the number of EEG acquisition channels is small, especially when the number of observed signals is less than that of the source signals, and the overcomplete problem will arise. The independent component analysis (ICA) algorithm commonly used for artifact removal requires the number of basis vectors to be smaller than the dimension of the input data due to a set of standard orthonormal bases learned during the convergence process, so it cannot be used to solve the overcomplete problem. The empirical mode decomposition method decomposes the signal into several independent intrinsic mode functions so that the number of observed signals is more than that of the source signals, solving the overcomplete problem. However, when using this method to solve overcompleteness, the modal aliasing problem will arise, which is caused by abnormal events such as sharp signals, impulse interference, and noise. Aiming at the above problems, we propose a novel EEG artifact removal method based on discrete wavelet transform, complete empirical mode decomposition for adaptive noise (CEEMDAN) and ICA in this paper. First, the input signals are transformed by discrete wavelet (DWT), and then CEEMDAN is used to solve the overcomplete and mode aliasing problems, meeting the a priori conditions of the ICA algorithm. Finally, the components belonging to EOG artifacts are removed according to the sample entropy value of each independent component. Experiments show that this method can effectively remove EOG artifacts while solving the overcomplete and modal aliasing problems.

Which BSS method separates better the EEG Signals? A comparison of five different algorithms

A very common strategy for rejecting electroencephalographic (EEG) artifacts, includes the decomposition of filtered EEG signals using a Blind Source Separation (BSS) algorithm, the identification and removal of artifactual components and the reconstruction of the cleaned EEG signals. In this pipeline, the performance of the BSS algorithm, which is defined as its ability to separate properly the independent sources (like the EEG from artifactual sources), is very crucial for rejecting most of the artifacts, while maintaining the most part of EEG intact. The overwhelming majority of the published papers uses the extended INFOMAX version of Independent Component Analysis (ICA) for artifact rejection purposes. But is this the most efficient algorithm to separate EEG signals into independent components? This study comes to shed light to the aforementioned question by assessing the performance of the five most common BSS algorithms. The normalized entropy of the brain-related components, their correlation between independent components with the original sources and the amount of the overall mutual information reduction (MIR) achieved by each decomposition were computed in datasets with systematically varying numbers of electrodes (ranging from 19 tο 99), from 26 real human scalp EEG recordings. Additionally, 54 different datasets containing artificially contaminated EEG signals were also examined for the same purpose, on the basis of the Euclidean distance and the correlation, between the generated Independent Components (ICs) and the original vertical and horizontal eye signals, which were used for the contamination. The results support that the Adaptive Mixture ICA was the best performing BSS method.

Improved ICA algorithm for ECG feature extraction and R‐peak detection

SummaryElectrocardiogram (ECG) signal transmission and monitoring plays a paramount role in long‐term cardiac monitoring and analysis to provide remote health care in time, especially for the postoperative people and people in remote areas. The accuracy of ECG signals is of fundamental importance in cardiac diagnosis like R‐peak detection. So we need to incorporate analytical methods in existing healthcare systems, to capture more meaningful ECG components and to represent physical cardiac sources more clearly. With this aim, hardware optimized FCAICA (fast confluence adaptive independent component analysis) algorithm is proposed to extract pure ECG components from the ECG mixtures. The extracted signals are then subjected to R‐peak detection for further analysis. The proposed improved fast confluence adaptive independent component analysis (IFCAICA) method occupies less hardware resources, consumes low power, improves accuracy, and sensitivity in R‐peaks detection. In 0.18 nm technology, the IFCAICA consumes 10.13 mW of power and operates with 3.4 MHz operating frequency.

An Adaptive ICA-Based Cross-Correlation Techniques for Water Pipeline Leakage Localization Utilizing Acousto-Optic Sensors

This paper describes the implementation of an adaptive ICA (Independent Component Analysis) based cross-correlation technique to process the noisy output from Acoustic optic sensor to locate the leaks in water pipes. In Digital Signal Processing methods, cross-correlation technique plays a prominent role in detecting the leak signals. In a real-time scenario, the leak signals get convolved with the impulse response of the pipe. In such cases, the conventional cross-correlation technique fails to detect the leak. This paper implements the maximization of non-gaussianity of ICA techniques along with wavelet decomposition as a preprocessing stage of frequency domain cross-correlation. The decomposition of the sensor data using wavelet reduces the computational complexity of the ICA. The adaptiveness of ICA in the proposed technique is more suitable for real-time water leak detection system. The probable benefit of using adaptive ICA is to enforce strong decorrelation and separation of the real-time leak signal from the convoluted impulse response of the pipe with noise. The above algorithm is validated using different experiments. The experimental result shows that the proposed method offers high accuracy of detecting the leak location compared to the conventional methods.

Adaptive Independent Component Analysis–Based Cross-Correlation Techniques along with Empirical Mode Decomposition for Water Pipeline Leakage Localization Utilizing Acousto-Optic Sensors

AbstractThe leak localization in a water pipeline system plays a vital role in pipeline system modeling. The leak location is identified by estimating the time difference between two sensor signals...

Independent Component-Based Spatiotemporal Clutter Filtering for Slow Flow Ultrasound.

Effective tissue clutter filtering is critical for non-contrast ultrasound imaging of slow blood flow in small vessels. Independent component analysis (ICA) has been considered by other groups for ultrasound clutter filtering in the past and was shown to be superior to principal component analysis (PCA)-based methods. However, it has not been considered specifically for slow flow applications or revisited since the onset of other slow flow-focused advancements in beamforming and tissue filtering, namely angled plane wave beamforming and full spatiotemporal singular value decomposition (SVD) (i.e., PCA-based) tissue filtering. In this work, we aim to develop a full spatiotemporal ICA-based tissue filtering technique facilitated by plane wave applications and compare it to SVD filtering. We compare ICA and SVD filtering in terms of optimal image quality in simulations and phantoms as well as in terms of optimal correlation to ground truth blood signal in simulations. Additionally, we propose an adaptive blood independent component sorting and selection method. We show that optimal and adaptive ICA can consistently separate blood from tissue better than principal component analysis (PCA)-based methods using simulations and phantoms. Additionally we demonstrate initial in vivo feasibility in ultrasound data of a liver tumor.

Noise reduction in speech signals using adaptive independent component analysis (ICA) for hands free communication devices

This paper aims to remove the noise presents in speech signals during communication in all hands-free devices like mobile phone, video conferencing, teleconferencing conferencing etc. The existing noise reduction algorithms like an adaptive filter, time-varying and multiband adaptive gain control etc., have serious drawbacks. To enhance the algorithm for a better outcome an independent component analysis (ICA) based noise reduction is used. ICA is a statistical computational technique that divides the multisource signal into individual subcomponents. It is an active approach to cancel all of the ambient noise or a selective part of it without knowing the knowledge of the background noise. The adaptive nature of ICA in the proposed method makes the algorithm more robust in a real-time scenario. In the proposed method, the noisy speech signal is maximized by using kurtosis and negentropy cost functions of ICA to separate out the original speech signal from the noise. The simulations show that the proposed adaptive ICA method provides higher SNR compared to existing ICA methods and other conventional methods. Thus Adaptive ICA performs efficient noise cancellation in all real-time communication devices.

Adaptive Selective Ensemble-Independent Component Analysis Models for Process Monitoring

Independent component analysis (ICA) has been widely used in non-Gaussian industrial process monitoring. However, the stability of performance and determination of dominant ICs are still the main problems for ICA. Constructing a monitoring model to achieve the best performance for different faults is a great challenge owing to the diversity and unknowability of faults. This study develops an adaptive selective ensemble ICA models method to improve the monitoring performance. Ensemble learning based on the bagging algorithm is adopted to enhance the stability of ICA. According to the difference of ICs selected for ICA modeling and hierarchical clustering, corresponding model sets are constructed for each sample subset. To ensure the accuracy of each submodel, an adaptive method based on just-in-time learning is proposed to model selection. Bayesian inference is applied to determine the final monitoring index. The validity of the proposed approach is attested through a numerical example, TE benchmark proces...

A Unifying Objective Function of Independent Component Analysis for Ordering Sources by Non-Gaussianity.

The independent component analysis (ICA) is a widely used method for solving blind separation problems. The ICA assumes that the sources are independent of each other and extracts them by maximizing their non-Gaussianity as the objective function. There are the two types of non-Gaussianity of the sources (the super-Gaussian type with the positive kurtosis and the sub-Gaussian one with the negative kurtosis). In this paper, we propose a new objective function unifying the two types of non-Gaussianity naturally, which is derived by applying the Gaussian approximation to the distribution of sources in the second-order polynomial feature space. The proposed objective function [called the adaptive ICA function (AIF)] is a simple form given as a summation of weighted fourth-order statistics, where the weights are adaptively estimated by the current kurtoses. The first practical advantage of the AIF is that it can extract the sources one by one in the descending order of the criterion of non-Gaussianity. It can solve the permutation ambiguity problem. The second and more important advantage is that it can estimate the number of non-Gaussian sources by the Akaike information criterion irrespective of the specific form of their distributions. In order to utilize the above-mentioned advantages of the AIF, we construct a new algorithm named the ordering ICA by extending the fast ICA. Experimental results verify that the ordering ICA can estimate the number of non-Gaussian sources correctly in both artificial and real data sets.

Adaptive Step Size Gradient Ascent ICA Algorithm for Wireless MIMO Systems

Independent component analysis (ICA) is a technique of blind source separation (BSS) used for separation of the mixed received signals. ICA algorithms are classified into adaptive and batch algorithms. Adaptive algorithms perform well in time-varying scenario with high-computational complexity, while batch algorithms have better separation performance in quasistatic channels with low-computational complexity. Amongst batch algorithms, the gradient-based ICA algorithms perform well, but step size selection is critical in these algorithms. In this paper, an adaptive step size gradient ascent ICA (ASS-GAICA) algorithm is presented. The proposed algorithm is free from selection of the step size parameter with improved convergence and separation performance. Different performance evaluation criteria are used to verify the effectiveness of the proposed algorithm. Performance of the proposed algorithm is compared with the FastICA and optimum block adaptive ICA (OBAICA) algorithms for quasistatic and time-varying wireless channels. Simulation is performed over quadrature amplitude modulation (QAM) and binary phase shift keying (BPSK) signals. Results show that the proposed algorithm outperforms the FastICA and OBAICA algorithms for a wide range of signal-to-noise ratio (SNR) and input data block lengths.

GRADIENT ASCENT INDEPENDENT COMPONENT ANALYSIS ALGORITHM FOR TELECOMMUNICATION SIGNALS

Independent Component Analysis (ICA) algorithm is normallused for un-mixing and feature extraction of the fixed input block lengths. In case of varying block lengths re-adjustment of the maximum number of iterations and the step size parameter is required. In this paper, we introduced an Adaptive Step size Gradient Ascent ICA (AS-GAICA) technique for varying block length that can also controls the maximum number of iterations adaptively. The performance of the proposed technique is compared with Fast-ICA and Optimum Block Adaptation ICA (OBAICA) for telecommunication signals. Simulation results show that the proposed scheme outperforms the Fast-ICA and OBAICA algorithms.

Blind channel estimation for massive MIMO

In order to scale with the demand of higher data rates and improved spectral efficiency in next generation wireless communication systems, a large-scale multiple-input and multiple-output (MIMO) technology called massive MIMO has been proposed. In massive MIMO, appropriate signal-to-noise ratio (SNR) values can be achieved by the addition of base station (BS) antennas in place of increasing transmit power. Pilot-based channel estimation is widely used in conventional MIMO systems, where pilot signal sequences are sent from the user terminals (UTs) to the BS to estimate the channel. In massive MIMO-based cellular networks, channel estimation in a given cell will be impaired by the pilot signal sequences transmitted by users in other cells--rendering the addition of antennas or transmit power ineffective. This effect is called pilot contamination. Therefore, pilot-based channel estimation limits the performance of massive MIMO. Semi-blind and blind methods are alternatives to pilot-based channel estimation that perform channel estimation with short pilot signal sequences and without pilot signal sequences, respectively. Blind channel estimation is one of the promising solutions to the pilot contamination problem in massive MIMO. This paper compares, using MATLAB simulations of a cluster-based COST 2100 channel model, the performance of pilot-based, semi-blind, blind, and adaptive-blind channel estimation methods. The pilot contamination effect on different channel estimation methods and how channel estimation methods can be used to overcome pilot contamination are shown. Finally, an adaptive independent component analysis (ICA)-based channel estimation method, which outperforms conventional ICA in terms of computational complexity, is proposed.

High-resolution EEG source imaging of one-year-old children.

Recently we described an iterative skull conductivity and source location estimation (SCALE) algorithm for simultaneously estimating head tissue conductivities and brain source locations. SCALE uses a realistic FEM forward problem head model and scalp maps of 10 or more near-dipolar sources identified by independent component analysis (ICA) decomposition of sufficient high-density EEG data. In this study, we applied SCALE to 20 minutes of 64-channel EEG data and magnetic resonance (MR) head images from four twelve-months-of-age infants. For each child, we selected 15-16 near-dipolar independent components from multiple-model adaptive mixture ICA (AMICA) decomposition of their EEG data. SCALE converged to brain-to-skull conductivity ratio (BSCR) estimates in the 10-12 range and mostly compact gyral or sulcal cortical distributions for the IC sources.

Adaptive Complex-Valued Independent Component Analysis Based on Second-Order Statistics

This paper proposes a two-stage fast convergence adaptive complex-valued independent component analysis based on second-order statistics of complex-valued source signals. The first stage constructs a cost function by extending the real-valued whiten cost function to a complex-valued domain and optimizes the cost function using a complex-valued gradient. The second stage uses the restriction that the pseudocovariance matrix of the separated signal is a diagonal matrix to construct the cost function and the geodesic method is used to optimize the cost function. Compared with other adaptive complex-valued independent component analysis, the proposed method shows a faster convergence rate and smaller error. Computer simulations were performed on synthesized signals and communications signals. The simulation results demonstrate the validity of the proposed algorithm.

Simultaneous head tissue conductivity and EEG source location estimation

Accurate electroencephalographic (EEG) source localization requires an electrical head model incorporating accurate geometries and conductivity values for the major head tissues. While consistent conductivity values have been reported for scalp, brain, and cerebrospinal fluid, measured brain-to-skull conductivity ratio (BSCR) estimates have varied between 8 and 80, likely reflecting both inter-subject and measurement method differences. In simulations, mis-estimation of skull conductivity can produce source localization errors as large as 3cm. Here, we describe an iterative gradient-based approach to Simultaneous tissue Conductivity And source Location Estimation (SCALE). The scalp projection maps used by SCALE are obtained from near-dipolar effective EEG sources found by adequate independent component analysis (ICA) decomposition of sufficient high-density EEG data. We applied SCALE to simulated scalp projections of 15cm2-scale cortical patch sources in an MR image-based electrical head model with simulated BSCR of 30. Initialized either with a BSCR of 80 or 20, SCALE estimated BSCR as 32.6. In Adaptive Mixture ICA (AMICA) decompositions of (45-min, 128-channel) EEG data from two young adults we identified sets of 13 independent components having near-dipolar scalp maps compatible with a single cortical source patch. Again initialized with either BSCR 80 or 25, SCALE gave BSCR estimates of 34 and 54 for the two subjects respectively. The ability to accurately estimate skull conductivity non-invasively from any well-recorded EEG data in combination with a stable and non-invasively acquired MR imaging-derived electrical head model could remove a critical barrier to using EEG as a sub-cm2-scale accurate 3-D functional cortical imaging modality.

On the convergence of ICA algorithms with weighted orthogonal constraint

A kind of weighted orthogonal constrained independent component analysis (ICA) algorithms with weighted orthogonalization for achieving this constraint is proposed recently. It has been proved in the literature that weighted orthogonal constrained ICA algorithms keep the equivariance property and have much better convergence speed, separation ability and steady state misadjustment, but the convergence is not yet analyzed in the published literature. The goal of this paper is to fill this gap. Firstly, a characterization of the stationary points corresponding to these algorithms using symmetric Minimum Distance Weighted Unitary Mapping (MDWUM) for achieving the weighted orthogonalization is obtained. Secondly, the monotonic convergence of the weighted orthogonal constrained fixed point ICA algorithms using symmetric MDWUM for convex contrast function is proved, which is further extended to nonconvex contrast functions case by adding a weighted orthogonal constraint term onto the contrast function. Together with the boundedness of contrast function, the convergence of fixed point ICA algorithms with weighted orthogonal constraint using symmetric MDWUM is implied. Simulation experiments results show that the adaptive ICA algorithms using symmetric MDWUM are better in terms of accuracy than the ones with pre-whitening, and the fixed-point ICA algorithms using symmetric MDWUM converge monotonically.

A Novel Floating Point Fast Confluence Adaptive Independent Component Analysis for Signal Processing Applications

Independent component analysis (ICA) is a technique that separates the independent source signals from their mixtures by minimizing the statistical dependence between components. This paper presents a floating point implementation of a novel fast confluence adaptive independent component analysis (FCAICA) technique with reduced number of iterations that provides the high convergence speed. Fixed point ICA algorithms cover only smaller range of numbers. To handle large as well as tiny numbers and hence to improve the dynamic range of the signal values, floating point operations are performed in ICA. The high convergence speed is achieved by a novel optimization scheme that adaptively changes the weight vector based on the kurtosis value. To validate the performance of the proposed FCAICA, simulation and synthesis are performed with super-gaussian mixtures and sub Gaussian mixtures and experimental results provided. The proposed FCAICA processor separates the super-Gaussian signals with a maximum operating frequency of 2.91MHz with improved convergence speed.

Quadratic Independent Component Analysis Based on Sparse Component

In this paper, a novel signal blind separation using adaptive multi-resolution independent component analysis based on sparse component is presented. This method separates mixed signal based on quadratic function and sparse representation. The quadratic function can be interpreted as the time-frequency function or time-scale function, or other. The sparse expression is the original signal through the dictionary to get their coefficients. Most of the coefficients is very small, close to zero, can greatly save separate computing time. At the same time this method can filter out the noise. The argorithm extends the separate technology from time-frequency domain to sparse mutil-resolution domain. The experimental result showed the method can be effective separation of mixed signals. And it shows that the method is feasible.

Image Blind Separation Based on Adaptive Multi-Resolution Independent Component Analysis

In this paper, a novel image blind separation using adaptive multi-resolution independent component analysis is presented.This method separates mixed images based on quadratic function. The quadratic function can be interpreted as the time-frequency function or time-scale function, or other. According to the signal characteristics, we can choose the frequency resolution or scale resolution. The argorithm extends the separate technology from one dimensional domain to two dimensional domain,and it’s implement by adaptive procedure. The experimental result showed the method can be effective separation of mixed images. And it shows that the method is feasible.