Independent Component Analysis Extracts Research Articles

Wavelet packet transform-based medical image multiple watermarking with independent component analysis extraction

Rapid growth of internet in all aspects of life has led to the easy availability of the digital data to everyone. E-commerce, telemedicine, etc., are among the many applications of internet. Telemedicine is a crucial field where internet finds application. Healthcare professionals use internet to transmit and receive medical data. Thus the medical images can be shared, processed and transmitted through computer networks. All patient records, linked to the medical secrecy, must be confidential. Because of the importance of the security issues in the management of medical information, there is a need to develop watermarking techniques for protecting medical images. In this paper, colour medical image watermarking methods rely on wavelet packet transform (WPT) and extraction using independent component analysis (ICA). For watermark extraction, Pearson ICA is applied as it attains the new trait is that it not entail the renovation procedure in watermark extraction. The grades show that projected method is vigorous beside attacks such as Gaussian noise, salt and pepper noise, rotation and translation. The performance measures like PSNR, similarity measure and normalised correlation are assessed to confirm the robustness of the scheme.

Wavelet packet transform-based medical image multiple watermarking with independent component analysis extraction

Rapid growth of internet in all aspects of life has led to the easy availability of the digital data to everyone. E-commerce, telemedicine, etc., are among the many applications of internet. Telemedicine is a crucial field where internet finds application. Healthcare professionals use internet to transmit and receive medical data. Thus the medical images can be shared, processed and transmitted through computer networks. All patient records, linked to the medical secrecy, must be confidential. Because of the importance of the security issues in the management of medical information, there is a need to develop watermarking techniques for protecting medical images. In this paper, colour medical image watermarking methods rely on wavelet packet transform (WPT) and extraction using independent component analysis (ICA). For watermark extraction, Pearson ICA is applied as it attains the new trait is that it not entail the renovation procedure in watermark extraction. The grades show that projected method is vigorous beside attacks such as Gaussian noise, salt and pepper noise, rotation and translation. The performance measures like PSNR, similarity measure and normalised correlation are assessed to confirm the robustness of the scheme.

A slow independent component analysis algorithm for time series feature extraction with the concurrent consideration of high-order statistic and slowness

For the process data analytics, numerous statistical methods are designed to extract informative features to reveal latent characteristics and correlation patterns among the variables. As a classic statistical method, independent component analysis extracts mutually independent components by searching for the maximum direction of non-Gaussianity from the mixed signals. However, the algorithm does not work for the latent Gaussian components, and the disorder of extracted features also brings trouble to the practical applications. In the present work, a slow independent component analysis algorithm is proposed to tackle the problems by concurrently considering the high-order statistic and slowness. It constructs a dual-objective optimization criterion function, simultaneously incorporating independence and slow varying characteristics into the feature extraction procedure, which ties together the ideas of independent component analysis and slow feature analysis under the same mathematical umbrella. Through adjusting the controllable sub-optimization objective weight, the proposed slow independent component analysis adds insight into the different roles of independence and slow varying characteristics in calibration modeling, and, thus, provides possibilities to combine the advantages of independent component analysis and slow feature analysis. Compared with the traditional method, the new algorithm no longer relies solely on higher-order statistic but also utilizes the slow varying characteristic to extract latent Gaussian or non-Gaussian components from mixed signals. Besides the unsupervised paradigm, the proposed algorithm is extended to the supervised form for classification task by utilizing the temporal structure as well as the label information. The effectiveness of the proposed algorithm is illustrated by blind source separation task and industrial fault diagnosis task using real-world datasets including speech signals and multiphase flow facility.

Improvement of BCI Performance Through Nonlinear Independent Component Analysis Extraction

Electroencephalogram (EEG) recordings provide an important means of brain-computer communication, but their classification accuracy and transfer rate are limited by unexpected signal variations due to artifacts and noises. In this paper, a nonlinear independent component analysis (NICA) extraction method for brain signal based EEG-P300 are proposed. The performance of the proposed method is investigated through a comparison of well known extraction methods (i.e., AAR, JADE, and SOBI algorithms). Finally, the promising results reported here reflect the considerable potential of EEG for the continuous classification of mental states.

ICA Extracts Epileptic Sources from fMRI in EEG-Negative Patients: A Retrospective Validation Study

Simultaneous EEG-fMRI has proven to be useful in localizing interictal epileptic activity. However, the applicability of traditional GLM-based analysis is limited as interictal spikes are often not seen on the EEG inside the scanner. Therefore, we aim at extracting epileptic activity purely from the fMRI time series using independent component analysis (ICA). To our knowledge, we show for the first time that ICA can find sources related to epileptic activity in patients where no interictal spikes were recorded in the EEG. The epileptic components were identified retrospectively based on the known localization of the ictal onset zone (IOZ). We demonstrate that the selected components truly correspond to epileptic activity, as sources extracted from patients resemble significantly better the IOZ than sources found in healthy controls. Furthermore, we show that the epileptic components in patients with and without spikes recorded inside the scanner resemble the IOZ in the same degree. We conclude that ICA of fMRI has the potential to extend the applicability of EEG-fMRI for presurgical evaluation in epilepsy.

A Modified Infomax ICA Algorithm for fMRI Data Source Separation

This study presents a modified infomax model of Independent Component Analysis (ICA) for the source separation problem of fMRI data. Functional MRI data is processed by different blind source separation techniques including Independent Component Analysis (ICA). ICA is a statistical decomposition method used for multivariate data source separation. ICA algorithm is based on independence of extracted sources for which different techniques are used like kurtosis, negentropy, information maximization etc. The infomax method of ICA extracts unknown sources from a number of mixtures by maximizing the negentropy thus ensuring independence. In this proposed modified infomax model a higher order contrast function is used which results in fast convergence and accuracy. The Proposed algorithm is applied to general simulated signals and simulated fMRI data. Comparison of correlation results of the proposed algorithm with the conventional infomax algorithm shows better performance.

ICA Spatiotemporal Filtering Method and Its Application in GPS Deformation Monitoring

Principal component analysis (PCA) is a good method to be used in spatiotemporal filtering for regional GPS network. As an extension of PCA, independent component analysis(ICA) is also widely concerded in many fields of sciences and application researches. As a new spatiotemporal filtering method, the application of ICA in spatiotemporal filtering of the regional GPS network and GPS deformation monitoring is explored in this paper. The simulated data test shows the filtering effect of ICA is the same as PCA, both of the PCA and ICA can extract two independent components which implied in simulated common mode error. At the same time, the SCIGN data test shows the filtering effect of ICA is a litter worse than PCA, but ICA extracts not only one independent components as common mode error, it is not unique and independence that can not be provided by the PCA method. It also reflects the essence of common mode error of different station in independence. Therefore, ICA method can be applied to GPS deformation monitoring as a new spatiotemporal filtering method, the feasibility and advantage of ICA is demonstrated in the experiment of simulated data and SCIGN data.

Robust extraction of P300 using constrained ICA for BCI applications

P300 is a positive event-related potential used by P300-brain computer interfaces (BCIs) as a means of communication with external devices. One of the main requirements of any P300-based BCI is accuracy and time efficiency for P300 extraction and detection. Among many attempted techniques, independent component analysis (ICA) is currently the most popular P300 extraction technique. However, since ICA extracts multiple independent components (ICs), its use requires careful selection of ICs containing P300 responses, which limits the number of channels available for computational efficiency. Here, we propose a novel procedure for P300 extraction and detection using constrained independent component analysis (cICA) through which we can directly extract only P300-relevant ICs. We tested our procedure on two standard datasets collected from healthy and disabled subjects. We tested our procedure on these datasets and compared their respective performances with a conventional ICA-based procedure. Our results demonstrate that the cICA-based method was more reliable and less computationally expensive, and was able to achieve 97 and 91.6% accuracy in P300 detection from healthy and disabled subjects, respectively. In recognizing target characters and images, our approach achieved 95 and 90.25% success in healthy and disabled individuals, whereas use of ICA only achieved 83 and 72.25%, respectively. In terms of information transfer rate, our results indicate that the ICA-based procedure optimally performs with a limited number of channels (typically three), but with a higher number of available channels (>3), its performance deteriorates and the cICA-based one performs better.

A statistical study of the Stromboli volcano explosion quakes before and during 2002–2003 eruptive crisis

[1] We study the seismic wavefield and the statistical properties of the Stromboli volcano explosions preceding and during the 2002–2003 crisis. We analyze the recordings of a three-component seismometer operating since 23 May 2002 to 30 January 2003, including the first 34 days of the crisis. Before the crisis, we recognize three bell-shaped classes of spectra with maxima falling in the range 1–5 Hz. Spectral content has two main changes, the most prominent one occurring at the crisis onset when the frequency peak at ∼0.3 Hz increases in amplitude. Independent component analysis extracts three time-stable independent oscillations that peaked at 1.1, 1.8, and 2.5 Hz, with radial and shallow polarization indicating a stable source mechanism. Energy of the explosions is lognormally distributed, except during a 2 month time interval before the crisis when it also shows a higher mean value. The interoccurrence time distributions display an homogeneous Poissonian behavior with a mean intertime of 250 s, without changes at the crisis onset. Only swarms of explosions are not ruled by a Poisson process and display higher occurrence rates and higher energies. Finally, we depict a scheme of the crisis. A modification of the equilibrium is induced by rising magma that produces a change in the boundary conditions of the plumbing system. The escape from the equilibrium produces, at first, variations in the usual statistics of the explosions, then it leads to the lava effusion and to a pressure drop in the plumbing system that induces a deep gas slug nucleation and the excitation of low frequencies.

Extraction of Neural Activity from In Vivo Optical Recordings Using Multiple Independent Component Analysis

A multiple independent component analysis (ICA) method based on the noisy time-delayed decorrelation algorithm is described that overcomes the problems and improves the usefulness of conventional ICA, which is commonly used for extracting the actual neural activity from data measured using optical recording with a voltage-sensitive dye to visualize neural activities in cortical areas as two-dimensional images. The problems with conventional ICA extraction include the lack of an a priori guarantee that the solution will be appropriate, the linear mixing of mutually independent random variables although the mixtures are not random variables but time signals in many applications, and the general requirement for repetitive calculation of large matrices. Application of multiple ICA to the extraction of neural activities in the guinea pig auditory cortex evoked by both click sounds and pure tones from optical recordings made using a voltage sensitive dye demonstrated that it effectively removes pulsatile and respiratory components from the measurement data and extracts neural activities from the optical recordings.

Localization of individual area neuronal activity

A family of methods, collectively known as independent component analysis (ICA), has recently been added to the array of methods designed to decompose a multi-channel signal into components. ICA methods have been applied to raw magnetoencephalography (MEG) and electroencephalography (EEG) signals to remove artifacts, especially when sources such as power line or cardiac activity generate strong components that dominate the signal. More recently, successful ICA extraction of stimulus-evoked responses has been reported from single-trial raw MEG and EEG signals. The extraction of weak components has often been erratic, depending on which ICA method is employed and even on what parameters are used. In this work, we show that if the emphasis is placed on individual "independent components," as is usually the case with standard ICA applications, differences in the results obtained for different components are exaggerated. We propose instead the reconstruction of regional brain activations by combining tomographic estimates of individual independent components that have been selected by appropriate spatial and temporal criteria. Such localization of individual area neuronal activity (LIANA) allows reliable semi-automatic extraction of single-trial regional activations from raw MEG data. We demonstrate the new method with three different ICA algorithms applied to both computer-generated signals and real data. We show that LIANA provides almost identical results with each ICA method despite the fact that each method yields different individual components.