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Results for “independent components analysis (ICA)”

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At least 19 recordsLinked to original sources

PubMed2003

A delayed class of BOLD waveforms associated with spreading depression in the feline cerebral cortex can be detected and characterised using independent component analysis (ICA).

An application of independent component analysis to blood oxygenation level- dependent MRI (BOLD-MRI) results was used to detect cerebrovascular changes that followed the initiation of cortical spreading depression (CSD) in feline brain. The cortical images were obtained from a horizontal plane at 28 s intervals before, and for 1.4-1.75 h after, KCl dissolved in agar (KCl/agar) had been directly applied to the left suprasylvian gyrus of 13 anesthetized cats for 10 min. It successfully resolved, for the first time, a novel class of prolonged, and delayed, biphasic BOLD waveforms. These were larger in amplitude ( approximately 20%), longer lasting and more delayed in onset (13-33 min) than the brief propagating (90 s) BOLD increase ( approximately 4%) already known to be associated with CSD on earlier occasions. Furthermore, such changes occurred in localized regions on the hemisphere ipsilateral to the site of stimulus application in 4 out of 5 control subjects rather than themselves generating propagating waves. Finally, the biphasic waveforms were consistently abolished in the 4 experimental animals studied following the i.v. administration of sumatriptan (0.3 mg kg(-1)), an antimigraine 5-HT(1B/1D) agonist, 15 min before the application of the transient stimulus. They were abolished in 2 out of 4 animals following the intraperitoneal (i.p.) administration of SB-220453 (tonabersat: 10 mg kg(-1), 90 min before stimulus application), a novel anticonvulsant that has recently been reported to inhibit CSD. ICA has thus been successful in detecting a novel localized, as opposed to propagating, signal of potential physiological significance hidden in complex BOLD- MRI data, whose sensitivity to sumatriptan may relate it to the cerebrovascular changes reported in the headache phase of migraine.

Animals↗

PubMedDate not supplied

[Independent Components Analysis (ICA) in the study of electroencephalographic signals].

Independent Component Analysis (ICA) is a mathematical tool able to separate complex signals in statistically independent components. It solves the blind source separation problem (BSS). The EEG satisfies most of the assumptions of ICA, so it may be an adequate signal for ICA for its use. In this paper we review the method and the applications of ICA in EEG. The studied applications are: a) artifacts removal; b) source estimation of spikes; c) analysis of seizures. Several studies have demonstrated that ICA is useful to remove artifacts from contaminated EEG records without distorting cerebral activity. It is able to decompose epileptic discharges and seizures in independent spatio-temporal components. In combination with techniques of source localisation, as dipole modelling, ICA improves the localization of the epileptic focus. Finally, we discuss the future role of ICA in the study of epileptic patients.

Algorithms↗

PubMed2004

Advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixtures.

In this paper, we review recent advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixing models. After a general introduction to BSS and ICA, we discuss in more detail uniqueness and separability issues, presenting some new results. A fundamental difficulty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they provide non-unique solutions without extra constraints, which are often implemented by using a suitable regularization. In this paper, we explore two possible approaches. The first one is based on structural constraints. Especially, post-nonlinear mixtures are an important special case, where a nonlinearity is applied to linear mixtures. For such mixtures, the ambiguities are essentially the same as for the linear ICA or BSS problems. The second approach uses Bayesian inference methods for estimating the best statistical parameters, under almost unconstrained models in which priors can be easily added. In the later part of this paper, various separation techniques proposed for post-nonlinear mixtures and general nonlinear mixtures are reviewed.

Bayes Theorem↗

PubMed2004

Separation of fetal and maternal magnetocardiographic signals in twin pregnancy using independent component analysis (ICA).

The identification of fetal and maternal signals in magnetocardiograms (MCG) is central to data preprocessing and a prerequisite for data analysis and assessment. This is usually done by creating a template of the signal to be identified and marking data segments correlating to this template before averaging. This procedure is not only cumbersome, but may also lead to problems when there are several overlapping signals of interest such as in MCG recording in single or, more so, in twin pregnancy. Independent component analysis (ICA), which uses higher order statistics to decompose the signal into statistical independent components, has already been used in single pregnancies to distinguish between maternal and fetal signals. We applied the ICA algorithm TDSEP to 9 data sets of twin pregnancies acquired between the 28th and 38th week of pregnancy. Resulting ICA components can be used for further data analysis, e.g., for finding robust triggers or estimating the heart rate and its variability of the twins. The results showed that the maternal and fetal components can be separated from each other as well as from other sources of noise and artifacts. Differences between averaged ICA time curves and averaged raw data are not significant. Limitations include a concurrence of heart rates and changes in signal morphology due to gross movement. Nonetheless, ICA offers a fast and efficient approach for the preprocessing of MCGs with multiple signals of interest.

Electrocardiography↗

PubMed2005

An independent components analysis (ICA) approach to the study of developmental differences in the saccadic contingent negative variation.

This study investigated the development of the saccadic CNV in 74 subjects aged 7-18 years, using pro- and anti-saccade tasks, and independent components analysis (ICA) for data analysis. Within the 2-stimulus paradigm, a central fixation point (S1) was followed 2.5s later by a peripheral cue (S2) presented at 4 degrees to the left or right of S1 in random order. The EEG was recorded from 40 electrodes applied over both hemispheres using a DC amplifier. With increasing age, pro- and anti-saccadic reaction times became faster, this effect being slightly more pronounced during childhood for the anti-saccade task. ICA revealed a lateral-posterior sCNV in younger children, and the known anterior-central sCNV in late adolescents. By contrast, the gaze maintenance negativity (GMNb), an anterior-central negativity accompanying the excursion of the eye, was present in all age groups. Our results underline the importance of topographical approaches in developmental ERP research and the usefulness of ICA. They suggest major task-dependent developmental differences in the spatial modulation of frontal-lobe sensitive ERP components.

Adolescent↗

PubMed2003

[Apply ICA (independent component analysis) to removing power noise from EEG].

Power noise is constantly found in EEG signals; thus the acquisition and analysis of EEG signals can be strongly influenced. Comparison of the efficiencies of four ICA algorithms (Fastica, Extended Infomax, EGLD, Pearson-ICA) and SVD methods in extracting power noise in the EEG signals showed that ICA algorithms appear insensitive to the noise disturbance, whereas the commonly used SVD method does not. By applying the Extended-Infomax ICA with better convergence in this paper, it was demonstrated that the power noise contained in the 16-channel EEG signals of one Alzheimer-disease patient were removed successfully(the lowest signal-noise-ratio for power noise is 0 dB). ICA has a possible important value and prospect in biomedical signal processing, especially in clinical medical engineering.

Algorithms↗

PubMed2005

Semi-blind ICA of fMRI: A method for utilizing hypothesis-derived time courses in a spatial ICA analysis.

Independent component analysis (ICA) is a data-driven approach utilizing high-order statistical moments to find maximally independent sources that has found fruitful application in functional magnetic resonance imaging (fMRI). Being a blind source separation technique, ICA does not require any explicit constraints upon the fMRI time courses. However, for some fMRI data analysis applications, such as for the analysis of an event-related paradigm, it would be useful to flexibly incorporate paradigm information into the ICA analysis. In this paper, we present an approach for constrained or semi-blind ICA (sbICA) analysis of event-related fMRI data by imposing regularization on certain estimated time courses using the paradigm information. We demonstrate the performance of our approach using both simulations and fMRI data from a three-stimulus auditory oddball paradigm. Simulation results suggest that (1) a regression approach slightly outperforms ICA when prior information is accurate and ICA outperforms the general linear model (GLM)-based approach when prior information is not completely accurate, (2) prior information improves the robustness of ICA in the presence of noise, and (3) ICA analysis using prior information with temporal constraints can outperform a regression approach when the prior information is not completely accurate. Using fMRI data, we compare a regression-based conjunction analysis of target and novel stimuli, both of which elicit an orienting response, to an sbICA approach utilizing both the target and novel stimuli to constrain the ICA time courses. Results show similar positive associations for both GLM and sbICA, but sbICA detects additional negative associates consistent with regions implicated in a default mode of brain activity. This suggests that task-related default mode decreases have a more "complex" signal that benefits from a flexible modeling approach. Compared with a traditional GLM approach, the sbICA approach provides a flexible way to analyze fMRI data that reduces the assumptions placed upon the hemodynamic response of the brain. The advantages and limitations of our technique are discussed in detail in the manuscript to provide guidelines to the reader for developing useful applications. The use of prior time course information in a spatial ICA analysis, which combines elements of both a regression approach and a blind ICA approach, may prove to be a useful tool for fMRI analysis.

Auditory Perception↗

PubMed2003

Latency (in)sensitive ICA. Group independent component analysis of fMRI data in the temporal frequency domain.

Independent component analysis (ICA), a data-driven approach utilizing high-order statistical moments to find maximally independent sources, has found fruitful application in functional magnetic resonance imaging (fMRI). A limitation of the standard fMRI ICA model is that a given component's time course is required to have the same delay at every voxel. As spatially varying delays (SVDs) may be found in fMRI data, using an ICA model with a fixed temporal delay for each source will have two implications. Larger SVDs can result in the splitting of regions with different delays into different components. Second, smaller SVDs can result in a biased ICA amplitude estimate due to only a slight delay difference. We propose a straightforward approach for incorporating this prior temporal information and removing the limitation of a fixed source delay by performing ICA on the amplitude spectrum of the original fMRI data (thus removing latency information). A latency map is then estimated for each component using the resulting component images and the raw data. We show that voxels with similar time courses, but different delays, are grouped into the same component. Additionally, when using traditional ICA, the amplitudes of motor areas are diminished due to systematic delay differences between visual and motor areas. The amplitudes are more accurately estimated when using a latency-insensitive ICA approach. The resulting time courses, the component maps, and the latency maps may prove useful as an addition to the collection of methods for fMRI data analysis.

Adult↗

PubMed2003

Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis.

Independent component analysis (ICA) is a technique which extracts statistically independent components from a set of measured signals. The technique enjoys numerous applications in biomedical signal analysis in the literature, especially in the analysis of electromagnetic (EM) brain signals. Standard implementations of ICA are restrictive mainly due to the square mixing assumption-for signal recordings which have large numbers of channels, the large number of resulting extracted sources makes the subsequent analysis laborious and highly subjective. There are many instances in neurophysiological analysis where there is strong a priori information about the signals being sought; temporally constrained ICA (cICA) can extract signals that are statistically independent, yet which are constrained to be similar to some reference signal which can incorporate such a priori information. We demonstrate this method on a synthetic dataset and on a number of artifactual waveforms identified in multichannel recordings of EEG and MEG. cICA repeatedly converges to the desired component within a few iterations and subjective analysis shows the waveforms to be of the expected morphologies and with realistic spatial distributions. This paper shows that cICA can be applied with great success to EM brain signal analysis, with an initial application in automating artifact extraction in EEG and MEG.

Algorithms↗

PubMed2006

Detecting functional connectivity in the resting brain: a comparison between ICA and CCA.

Independent component analysis (ICA) and cross-correlation analysis (CCA) are general tools for detecting resting-state functional connectivity. In this study, we jointly evaluated these two approaches based on simulated data and in vivo functional magnetic resonance imaging data acquired from 10 resting healthy subjects. The influence of the number of independent components (maps) on the results of ICA was investigated. The influence of the selection of the seeds on the results of CCA was also examined. Our results reveal that significant differences between these two approaches exist. The performance of ICA is superior as compared with that of CCA; in addition, the performance of ICA is not significantly affected by structured noise over a relatively large range. The results of ICA could be affected by the number of independent components if this number is too small, however. Converting the spatially independent maps of ICA into z maps for thresholding tends to overestimate the false-positive rate. However, the overestimation is not very severe and may be acceptable in most cases. The results of CCA are dependent on seeds location. Seeds selected based on different criteria will significantly affect connectivity maps.

Adolescent↗

PubMed2005

Independent component analysis of generalized spike-and-wave discharges: primary versus secondary bilateral synchrony.

OBJECTIVE: To differentiate between primary and secondary bilateral synchrony (PBS and SBS, respectively) in generalized spike-and-wave discharges (GSWD), we applied independent component analysis (ICA) to GSWD, and analyzed the characteristic patterns of independent components. METHODS: EEGs from 19 patients with GSWD (9 PBS patients, 10 SBS patients) were studied. Thirty GSWD epochs were selected and concatenated to construct an EEG data matrix that was subjected to ICA. Selected independent components were localized by mapping them on a spherical model of the head by means of brain electrical source analysis (BESA) to define dipole sources. RESULTS: Epileptic components of GSWD were clearly separated by the ICA algorithm in all patients. Between one and three components per patient were responsible for GSWD. In PBS patients, 70.6% of the independent components had dipole sources within the dorsolateral frontal region, and 56.5% of the independent components in SBS patients were within the medial frontal region (P=0.012). The orientation of all of the independent components in PBS patients was radial, whereas 47.8% of the independent components in SBS patients were tangential; this difference was statistically significant (P=0.001). CONCLUSIONS: Spatiotemporal decomposition of GSWD by using ICA might be helpful for differentiating SBS from PBS. SIGNIFICANCE: The localization of the dipole sources of the independent components may provide insight into the pathophysiological origins of GSWD.

Action Potentials↗

PubMed2003

Application of independent component analysis to microarrays.

We apply linear and nonlinear independent component analysis (ICA) to project microarray data into statistically independent components that correspond to putative biological processes, and to cluster genes according to over- or under-expression in each component. We test the statistical significance of enrichment of gene annotations within clusters. ICA outperforms other leading methods, such as principal component analysis, k-means clustering and the Plaid model, in constructing functionally coherent clusters on microarray datasets from Saccharomyces cerevisiae, Caenorhabditis elegans and human.

Algorithms↗

PubMed2004

BCI Competition 2003--Data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications.

An algorithm based on independent component analysis (ICA) is introduced for P300 detection. After ICA decomposition, P300-related independent components are selected according to the a priori knowledge of P300 spatio-temporal pattern, and clear P300 peak is reconstructed by back projection of ICA. Applied to the dataset IIb of BCI Competition 2003, the algorithm achieved an accuracy of 100% in P300 detection within five repetitions.

Algorithms↗

PubMedDate not supplied

Independent component analysis as a tool to eliminate artifacts in EEG: a quantitative study.

Independent component analysis (ICA) is a novel technique that calculates independent components from mixed signals. A hypothetical clinical application is to remove artifacts in EEG. The goal of this study was to apply ICA to standard EEG recordings to eliminate well-known artifacts, thus quantifying its efficacy in an objective way. Eighty samples of recordings with spikes and evident artifacts of electrocardiogram (EKG), eye movements, 50-Hz interference, muscle, or electrode artifact were studied. ICA components were calculated using the Joint Approximate Diagonalization of Eigen-matrices (JADE) algorithm. The signal was reconstructed excluding those components related to the artifacts. A normalized correlation coefficient was used as a measure of the changes caused by the suppression of these components. ICA produced an evident clearing-up of signals in all the samples. The morphology and the topography of the spike were very similar before and after the removal of the artifacts. The correlation coefficient showed that the rest of the signal did not change significantly. Two examiners independently looked at the samples to identify the changes in the morphology and location of the discharge and the artifacts. In conclusion, ICA proved to be a useful tool to clean artifacts in short EEG samples, without having the disadvantages associated with the digital filters. The distortion of the interictal activity measured by correlation analysis was minimal.

Algorithms↗

PubMed2005

What is novel in the novelty oddball paradigm? Functional significance of the novelty P3 event-related potential as revealed by independent component analysis.

To better understand whether voluntary attention affects how the brain processes novel events, variants of the auditory novelty oddball paradigm were presented to two different groups of human volunteers. One group of subjects (n=16) silently counted rarely presented 'infrequent' tones (p=0.10), interspersed with 'novel' task-irrelevant unique environmental sounds (p=0.10) and frequently presented 'standard' tones (p=0.80). A second group of subjects (n=17) silently counted the 'novel' environmental sounds, the 'infrequent' tones now serving as the task-irrelevant deviant events. Analysis of event-related potentials (ERPs) recorded from 63 scalp channels suggested a spatiotemporal overlap of fronto-central novelty P3 and centro-parietal P3 (P3b) ERP features in both groups. Application of independent component analysis (ICA) to concatenated single trials revealed two independent component clusters that accounted for portions of the novelty P3 and P3b response features, respectively. The P3b-related ICA cluster contributed to the novelty P3 amplitude response to novel environmental sounds. In contrast to the scalp ERPs, the amplitude of the novelty P3 related cluster was not affected by voluntary attention, that is, by the target/nontarget distinction. This result demonstrates the usefulness of ICA for disentangling spatiotemporally overlapping ERP processes and provides evidence that task irrelevance is not a necessary feature of novelty processing.

Acoustic Stimulation↗

PubMed2006

Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach.

Independent component analysis (ICA) proves to be effective in the removing the ocular artifact from electroencephalogram recordings (EEG). While using ICA in ocular artifact correction, a crucial step is to correctly identify the artifact components among the decomposed independent components. In most previous works, this step of selecting the artifact components was manually implemented, which is time consuming and inconvenient when dealing with a large amount of EEG data. We present a new method which automatically selects the eye blink artifact components based on the pattern of their scalp topographies, which can be exemplified as a template matching approach. The feasibility of using a fixed template for singling out the eye blink component after ICA decomposition was validated by an experiment in which 18 subjects among the 21 subjects involved exhibited a highly consistent pattern of eye blink scalp topographies. Since only the spatial feature is employed for singling out the eye blink component, the proposed method is very efficient and easy to implement. Objective evaluation of the real results shows that the proposed algorithm can remove the eye blink artifact from the EEG while causing little distortion to the underlying brain activities.

Algorithms↗

PubMed2004

Metabolite fingerprinting: detecting biological features by independent component analysis.

MOTIVATION: Metabolite fingerprinting is a technology for providing information from spectra of total compositions of metabolites. Here, spectra acquisitions by microchip-based nanoflow-direct-infusion QTOF mass spectrometry, a simple and high throughput technique, is tested for its informative power. As a simple test case we are using Arabidopsis thaliana crosses. The question is how metabolite fingerprinting reflects the biological background. In many applications the classical principal component analysis (PCA) is used for detecting relevant information. Here a modern alternative is introduced-the independent component analysis (ICA). Due to its independence condition, ICA is more suitable for our questions than PCA. However, ICA has not been developed for a small number of high-dimensional samples, therefore a strategy is needed to overcome this limitation. RESULTS: To apply ICA successfully it is essential first to reduce the high dimension of the dataset, by using PCA. The number of principal components determines the quality of ICA significantly, therefore we propose a criterion for estimating the optimal dimension automatically. The kurtosis measure is used to order the extracted components to our interest. Applied to our A. thaliana data, ICA detects three relevant factors, two biological and one technical, and clearly outperforms the PCA.

Algorithms↗

PubMed2003

Cost component analysis.

In optimizations the dimension of the problem may severely, sometimes exponentially increase optimization time. Parametric function approximatiors (FAPPs) have been suggested to overcome this problem. Here, a novel FAPP, cost component analysis (CCA) is described. In CCA, the search space is resampled according to the Boltzmann distribution generated by the energy landscape. That is, CCA converts the optimization problem to density estimation. Structure of the induced density is searched by independent component analysis (ICA). The advantage of CCA is that each independent ICA component can be optimized separately. In turn, (i) CCA intends to partition the original problem into subproblems and (ii) separating (partitioning) the original optimization problem into subproblems may serve interpretation. Most importantly, (iii) CCA may give rise to high gains in optimization time. Numerical simulations illustrate the working of the algorithm.

Algorithms↗