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Source Separation (source + separation)

Distribution by Scientific Domains

Life Sciences	41%
Medical Sciences	33%

Selected Abstracts

Functional source separation applied to induced visual gamma activity

HUMAN BRAIN MAPPING, Issue 2 2008
Giulia Barbati
Abstract Objective of this work was to explore the performance of a recently introduced source extraction method, FSS (Functional Source Separation), in recovering induced oscillatory change responses from extra-cephalic magnetoencephalographic (MEG) signals. Unlike algorithms used to solve the inverse problem, FSS does not make any assumption about the underlying biophysical source model; instead, it makes use of task-related features (functional constraints) to estimate source/s of interest. FSS was compared with blind source separation (BSS) approaches such as Principal and Independent Component Analysis, PCA and ICA, which are not subject to any explicit forward solution or functional constraint, but require source uncorrelatedness (PCA), or independence (ICA). A visual MEG experiment with signals recorded from six subjects viewing a set of static horizontal black/white square-wave grating patterns at different spatial frequencies was analyzed. The beamforming technique Synthetic Aperture Magnetometry (SAM) was applied to localize task-related sources; obtained spatial filters were used to automatically select BSS and FSS components in the spatial area of interest. Source spectral properties were investigated by using Morlet-wavelet time-frequency representations and significant task-induced changes were evaluated by means of a resampling technique; the resulting spectral behaviours in the gamma frequency band of interest (20,70 Hz), as well as the spatial frequency-dependent gamma reactivity, were quantified and compared among methods. Among the tested approaches, only FSS was able to estimate the expected sustained gamma activity enhancement in primary visual cortex, throughout the whole duration of the stimulus presentation for all subjects, and to obtain sources comparable to invasively recorded data. Hum Brain Mapp 29:131,141, 2008. © 2007 Wiley-Liss, Inc. [source]

Survey of sparse and non-sparse methods in source separation

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 1 2005
Paul D. O'Grady
Abstract Source separation arises in a variety of signal processing applications, ranging from speech processing to medical image analysis. The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing process and by making assumptions about the sources. When the information about the mixing process and sources is limited, the problem is called ,blind'. By assuming that the sources can be represented sparsely in a given basis, recent research has demonstrated that solutions to previously problematic blind source separation problems can be obtained. In some cases, solutions are possible to problems intractable by previous non-sparse methods. Indeed, sparse methods provide a powerful approach to the separation of linear mixtures of independent data. This paper surveys the recent arrival of sparse blind source separation methods and the previously existing non-sparse methods, providing insights and appropriate hooks into theliterature along the way. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15;18,33;2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20035 [source]

Removing Eye-movement Artifacts from the EEG during the Intracarotid Amobarbital Procedure

EPILEPSIA, Issue 3 2005
Weidong Zhou
Summary:,Purpose: The EEG is often recorded during the intracarotid amobarbital procedure (IAP) to help in the assessment of the spatial extent and the duration of the effect of the drug. In scalp recordings, the EEG is always heavily contaminated with eye movement artifacts as the patient actively performs visual tasks. Methods: Independent component analysis (ICA) is a new technique for blind source separation. In this study, we separated the EEG data recorded during the IAP into independent components using ICA. The EEG signal was reconstructed by excluding the components related to eye movement and eye blinks. Results: EEGs from 10 IAP tests were analyzed. The experimental results indicate that ICA is very efficient at subtracting eye-movement artifacts, while retaining the EEG slow waves and making their interpretation easier. Conclusions: ICA appears to be a generally applicable and effective method for removing ocular artifacts from EEG recordings during IAP, although slow waves and ocular artifacts share similar frequency distributions. [source]

Tracing energy flow in stream food webs using stable isotopes of hydrogen

FRESHWATER BIOLOGY, Issue 5 2010
JACQUES C. FINLAY
Summary 1. Use of the natural ratios of carbon and nitrogen stable isotopes as tracers of trophic interactions has some clear advantages over alternative methods for food web analyses, yet is limited to situations where organic materials of interest have adequate isotopic separation between potential sources. This constrains the use of natural abundance stable isotope approaches to a subset of ecosystems with biogeochemical conditions favourable to source separation. 2. Recent studies suggest that stable hydrogen isotopes (,D) could provide a robust tracer to distinguish contributions of aquatic and terrestrial production in food webs, but variation in ,D of consumers and their organic food sources are poorly known. To explore the utility of the stable hydrogen isotope approach, we examined variation in ,D in stream food webs in a forested catchment where variation in ,13C has been described previously. 3. Although algal ,D varied by taxa and, to a small degree, between sites, we found consistent and clear separation (by an average of 67,) from terrestrial carbon sources. Environmental conditions known to affect algal ,13C, such as water velocity and stream productivity did not greatly influence algal ,D, and there was no evidence of seasonal variation. In contrast, algal ,13C was strongly affected by environmental factors both within and across sites, was seasonally variable at all sites, and partially overlapped with terrestrial ,13C in all streams with catchment areas larger than 10 km2. 4. While knowledge of isotopic exchange with water and trophic fractionation of ,D for aquatic consumers is limited, consistent source separation in streams suggests that ,D may provide a complementary food web tracer to ,13C in aquatic food webs. Lack of significant seasonal or spatial variation in ,D is a distinct advantage over ,13C for applications in many aquatic ecosystems. [source]

Distance separated simultaneous sweeping, for fast, clean, vibroseis acquisition

GEOPHYSICAL PROSPECTING, Issue 1 2010
Jack Bouska
ABSTRACT Distance separated simultaneous sweeping DS3 is a new vibroseis technique that produces independent records, uncontaminated by simultaneous source interference, for a range of offsets and depths that span all target zones of interest. Use of DS3 on a recent seismic survey in Oman, resulted in a peak acquisition rate of 1024 records per hour. This survey employed 15 vibrators, with a distance separation of 12 km between simultaneous active sources, recorded by 8000 active channels across 22 live lines in an 18.5 km × 11 km receiver patch. Broad distribution of simultaneous sources, across an adequately sized recording patch, effectively partitions the sensors so that each trace records only one of the simultaneous sources. With proper source separation, on a scale similar to twice the maximum usable source receiver offset, wavefield overlap occurs below the zone of interest. This yields records that are indistinguishable from non-simultaneous source data, within temporal and spatial limits. This DS3 technique may be implemented using a wide variety of acquisition geometries, optimally with spatially large recording patches that enable appropriate source separation distances. DS3 improves acquisition efficiency without data quality degradation, eliminating the requirement for special data processing or noise attenuation. [source]

Functional source separation applied to induced visual gamma activity

Blind separation of delayed instantaneous mixtures: a cross-correlation based approach

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 3 2004
Muhammad Z. Ikram
Abstract A cross-correlation based method is proposed for blind separation of statistically uncorrelated i.i.d. signals. In contrast to much of the existing work in the area, the proposed method allows the separation of more sources than sensors and the sensors are not restricted to have non-Gaussian distributions. The mixed signals are modelled as a sum of attenuated and delayed source signals. As compared to the delayless mixture model commonly employed in the literature, incorporating delays in the model may offer better fit to practical applications, such as source separation in an anechoic environment. We estimate the delay and attenuation parameters from the peak locations and strengths of the cross-correlation function, respectively. Using these parameters, we then discuss the use of four methods for the recovery of source signals. These methods are compared and their usage is proposed under different operating conditions. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Survey of sparse and non-sparse methods in source separation

Application of independent component analysis with mixture density model to localize brain alpha activity in fMRI and EEG

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 4 2004
Jeong-Won Jeong
Abstract Independent component analysis (ICA) is an approach to solve the blind source separation problem. In the original and extended versions of ICA, nonlinearity functions are fixed to have specific density forms such as super-Gaussian or sub-Gaussian, thereby limiting their performance when sources with different classes of densities are mixed in multichannel data. In this article, we have incorporated a mixture density model such that no assumption about source density would be required. We show that this leads to better source separation due to increased flexibility in handling source- densities with flexible parametric nonlinearity. The algorithm was validated through simulation studies and its performance was compared to other versions of ICA. The modified mixture density ICA was then applied to functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data to localize independent sources of alpha activity in the human brain. A good spatial correlation was found in the spatial distribution of alpha sources derived independently from fMRI and EEG, suggesting that spontaneous alpha rhythm can be imaged by fMRI using ICA without concurrent acquisition of EEG. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 170,180, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20021 [source]

Multi-component analysis: blind extraction of pure components mass spectra using sparse component analysis

JOURNAL OF MASS SPECTROMETRY (INCORP BIOLOGICAL MASS SPECTROMETRY), Issue 9 2009
Ivica Kopriva
Abstract The paper presents sparse component analysis (SCA)-based blind decomposition of the mixtures of mass spectra into pure components, wherein the number of mixtures is less than number of pure components. Standard solutions of the related blind source separation (BSS) problem that are published in the open literature require the number of mixtures to be greater than or equal to the unknown number of pure components. Specifically, we have demonstrated experimentally the capability of the SCA to blindly extract five pure components mass spectra from two mixtures only. Two approaches to SCA are tested: the first one based on ,1 norm minimization implemented through linear programming and the second one implemented through multilayer hierarchical alternating least square nonnegative matrix factorization with sparseness constraints imposed on pure components spectra. In contrast to many existing blind decomposition methods no a priori information about the number of pure components is required. It is estimated from the mixtures using robust data clustering algorithm together with pure components concentration matrix. Proposed methodology can be implemented as a part of software packages used for the analysis of mass spectra and identification of chemical compounds. Copyright © 2009 John Wiley & Sons, Ltd. [source]