The Experts below are selected from a list of 92946 Experts worldwide ranked by ideXlab platform
James J Pekar - One of the best experts on this subject based on the ideXlab platform.
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a method for making group inferences from functional mri data using Independent Component Analysis
Human Brain Mapping, 2001Co-Authors: Vince D Calhoun, Tulay Adali, Godfrey D Pearlson, James J PekarAbstract:Independent Component Analysis (ICA) is a promising Analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent Component Analysis has been successfully utilized to analyze single-subject fMRI data sets, and an extension of this work would be to provide for group inferences. However, unlike univariate methods (e.g., regression Analysis, Kolmogorov-Smirnov statistics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for drawing group inferences using ICA of fMRI data, and present its application to a simple visual paradigm that alternately stimulates the left or right visual field. Our group ICA Analysis revealed task-related Components in left and right visual cortex, a transiently task-related Component in bilateral occipital/parietal cortex, and a non-task-related Component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI Analysis method such as: (1) How many Components should be calculated? (2) How are these Components to be combined across subjects? (3) How should the final results be thresholded and/or presented? We show that the methodology we present provides answers to these questions and lay out a process for making group inferences from fMRI data using Independent Component Analysis.
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a method for making group inferences from functional mri data using Independent Component Analysis
Human Brain Mapping, 2001Co-Authors: Vince D Calhoun, Tulay Adali, Godfrey D Pearlson, James J PekarAbstract:Independent Component Analysis (ICA) is a promising Analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent Component Analysis has been successfully utilized to analyze single-subject fMRI data sets, and an extension of this work would be to provide for group inferences. However, unlike univariate methods (e.g., regression Analysis, Kolmogorov–Smirnov statistics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for drawing group inferences using ICA of fMRI data, and present its application to a simple visual paradigm that alternately stimulates the left or right visual field. Our group ICA Analysis revealed task-related Components in left and right visual cortex, a transiently task-related Component in bilateral occipital/parietal cortex, and a non-task-related Component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI Analysis method such as: (1) How many Components should be calculated? (2) How are these Components to be combined across subjects? (3) How should the final results be thresholded and/or presented? We show that the methodology we present provides answers to these questions and lay out a process for making group inferences from fMRI data using Independent Component Analysis. Hum. Brain Mapping 14:140–151, 2001. © 2001 Wiley-Liss, Inc.
Sheng Chen - One of the best experts on this subject based on the ideXlab platform.
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a process monitoring method based on noisy Independent Component Analysis
Neurocomputing, 2014Co-Authors: Xuemin Tian, Sheng ChenAbstract:Independent Component Analysis (ICA) is an effective feature extraction tool for process monitoring. However, the conventional ICA-based process monitoring methods usually adopt noise-free ICA models and thus may perform unsatisfactorily under the adverse effects of the measurement noise. In this paper, a process monitoring method using a new noisy Independent Component Analysis, referred to as NoisyICAn, is proposed. Using the noisy ICA model which considers the measurement noise explicitly, a NoisyICAn algorithm is developed to estimate the mixing matrix by setting up a series of the fourth-order cumulant matrices of the measured data and performing the joint diagonalization of these matrices. The kurtosis relationships of the Independent Components and measured variables are subsequently obtained based on the estimated mixing matrix, for recursively estimating the kurtosis of Independent Components. Two monitoring statistics are then built to detect process faults using the obtained recursive estimate of the Independent Components' kurtosis. The simulation studies are carried out on a simple three-variable system and a continuous stirred tank reactor system, and the results obtained demonstrate that the proposed NoisyICAn-based monitoring method outperforms the conventional noise-free ICA-based monitoring methods as well as the benchmark monitoring methods based on the existing noisy ICA schemes adopted from blind source separation, in terms of the fault detection time and local fault detection rate.
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multiway kernel Independent Component Analysis based on feature samples for batch process monitoring
Neurocomputing, 2009Co-Authors: Xuemin Tian, Xiaoling Zhang, Xiaogang Deng, Sheng ChenAbstract:Most batch processes generally exhibit the characteristics of nonlinear variation. In this paper, a nonlinear monitoring technique is proposed using a multiway kernel Independent Component Analysis based on feature samples (FS-MKICA). This approach first unfolds the three-way dataset of a batch process into the two-way one and then chooses representative feature samples from the large two-way input training dataset. The nonlinear feature space abstracted from the unfolded two-way data space is then transformed into a high-dimensional linear space via kernel function and an Independent Component Analysis (ICA) model is established in the mapped linear space. The proposed FS-MKICA method can significantly reduce the computational cost in extracting the kernel ICA model since it is based on the small subset of feature samples rather than on the entire input sample set. We supply two statistics, the I^2 statistic of process variation and the squared prediction error statistic of residual, for on-line monitoring of batch processes. The proposed method is applied to detecting faults in the fed-batch penicillin fermentation process. The standard linear ICA method for batch process monitoring, known as the multiway Independent Component Analysis (MICA), is also applied to the same benchmark batch process. The simulation results obtained in this nonlinear batch process application clearly demonstrate the power and superiority of the new nonlinear monitoring method over the linear one. The FS-MKICA approach can extract the nonlinear features of the batch process while the MICA method cannot.
Aapo Hyvärinen - One of the best experts on this subject based on the ideXlab platform.
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Independent Component Analysis recent advances
Philosophical Transactions of the Royal Society A, 2013Co-Authors: Aapo HyvärinenAbstract:Independent Component Analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find Components that are maximally Independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying Components, in contrast to classical methods. The basic theory of Independent Component Analysis was mainly developed in the 1990s and summarized, for example, in our monograph in 2001. Here, we provide an overview of some recent developments in the theory since the year 2000. The main topics are: Analysis of causal relations, testing Independent Components, analysing multiple datasets (three-way data), modelling dependencies between the Components and improved methods for estimating the basic model.
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topographic Independent Component Analysis
Neural Computation, 2001Co-Authors: Aapo Hyvärinen, Patrik O Hoyer, Mika InkiAbstract:In ordinary Independent Component Analysis, the Components are assumed to be completely Independent, and they do not necessarily have any meaningful order relationships. In practice, however, the estimated "Independent" Components are often not at all Independent. We propose that this residual dependence structure could be used to define a topographic order for the Components. In particular, a distance between two Components could be defined using their higher-order correlations, and this distance could be used to create a topographic representation. Thus, we obtain a linear decomposition into approximately Independent Components, where the dependence of two Components is approximated by the proximity of the Components in the topographic representation.
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Independent Component Analysis
2001Co-Authors: Aapo Hyvärinen, Juha Karhunen, Erkki OjaAbstract:In this chapter, we discuss a statistical generative model called Independent Component Analysis. It is basically a proper probabilistic formulation of the ideas underpinning sparse coding. It shows how sparse coding can be interpreted as providing a Bayesian prior, and answers some questions which were not properly answered in the sparse coding framework.
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Independent Component Analysis: Algorithms and Applications
Neural Networks, 2000Co-Authors: Aapo Hyvärinen, Anne Hyvarinen, Erkki OjaAbstract:A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each Component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal Component Analysis, factor Analysis, and projection pursuit. Independent Component Analysis (ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data so that the Components are statistically Independent, or as Independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. In this paper, we present the basic theory and applications of ICA, and our recent work on the subject.
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gaussian moments for noisy Independent Component Analysis
IEEE Signal Processing Letters, 1999Co-Authors: Aapo HyvärinenAbstract:A novel approach for the problem of estimating the data model of Independent Component Analysis (or blind source separation) in the presence of Gaussian noise is introduced. We define the Gaussian moments of a random variable as the expectations of the Gaussian function (and some related functions) with different scale parameters, and show how the Gaussian moments of a random variable can be estimated from noisy observations. This enables us to use Gaussian moments as one-unit contrast functions that have no asymptotic bias even in the presence of noise, and that are robust against outliers. To implement the maximization of the contrast functions based on Gaussian moments, a modification of the fixed-point (FastICA) algorithm is introduced.
Vince D Calhoun - One of the best experts on this subject based on the ideXlab platform.
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unmixing fmri with Independent Component Analysis
IEEE Engineering in Medicine and Biology Magazine, 2006Co-Authors: Vince D Calhoun, Tulay AdaliAbstract:Independent Component Analysis (ICA) is a statistical method used to discover hidden factors (sources or features) from a set of measurements or observed data such that the sources are maximally Independent. Typically, it assumes a generative model where observations are assumed to be linear mixtures of Independent sources and works with higher-order statistics to achieve independence. ICA has recently demonstrated considerable promise in characterizing functional magnetic resonance imaging (fMRI) data, primarily due to its intuitive nature and ability for flexible characterization of the brain function. In this article, ICA is introduced and its application to fMRI data Analysis is reviewed.
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a method for making group inferences from functional mri data using Independent Component Analysis
Human Brain Mapping, 2001Co-Authors: Vince D Calhoun, Tulay Adali, Godfrey D Pearlson, James J PekarAbstract:Independent Component Analysis (ICA) is a promising Analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent Component Analysis has been successfully utilized to analyze single-subject fMRI data sets, and an extension of this work would be to provide for group inferences. However, unlike univariate methods (e.g., regression Analysis, Kolmogorov-Smirnov statistics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for drawing group inferences using ICA of fMRI data, and present its application to a simple visual paradigm that alternately stimulates the left or right visual field. Our group ICA Analysis revealed task-related Components in left and right visual cortex, a transiently task-related Component in bilateral occipital/parietal cortex, and a non-task-related Component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI Analysis method such as: (1) How many Components should be calculated? (2) How are these Components to be combined across subjects? (3) How should the final results be thresholded and/or presented? We show that the methodology we present provides answers to these questions and lay out a process for making group inferences from fMRI data using Independent Component Analysis.
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a method for making group inferences from functional mri data using Independent Component Analysis
Human Brain Mapping, 2001Co-Authors: Vince D Calhoun, Tulay Adali, Godfrey D Pearlson, James J PekarAbstract:Independent Component Analysis (ICA) is a promising Analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent Component Analysis has been successfully utilized to analyze single-subject fMRI data sets, and an extension of this work would be to provide for group inferences. However, unlike univariate methods (e.g., regression Analysis, Kolmogorov–Smirnov statistics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for drawing group inferences using ICA of fMRI data, and present its application to a simple visual paradigm that alternately stimulates the left or right visual field. Our group ICA Analysis revealed task-related Components in left and right visual cortex, a transiently task-related Component in bilateral occipital/parietal cortex, and a non-task-related Component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI Analysis method such as: (1) How many Components should be calculated? (2) How are these Components to be combined across subjects? (3) How should the final results be thresholded and/or presented? We show that the methodology we present provides answers to these questions and lay out a process for making group inferences from fMRI data using Independent Component Analysis. Hum. Brain Mapping 14:140–151, 2001. © 2001 Wiley-Liss, Inc.
Terrence J Sejnowski - One of the best experts on this subject based on the ideXlab platform.
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complex spectral domain Independent Component Analysis of electroencephalographic data
2003Co-Authors: Terrence J Sejnowski, Scott MakeigAbstract:Independent Component Analysis (ICA) has proved to be a highly useful tool for modeling brain data and in particular electroencephalographic (EEG) data. In this paper, a new method is presented that may better capture the underlying source dynamics than ICA algorithms hereto employed for brain signal Analysis. We suppose that a brief, impulse-like activation of an effective signal source elicits a short sequence of spatio-temporal activations in the measured signals. This leads to a model of convolutive signal superposition, in contrast to the instantaneous mixing model commonly assumed for Independent Component Analysis of brain signals. In the spectral-domain, convolutive mixing is equivalent to multiplicative mixing of complex signal sources within distinct spectral bands. We decompose the recorded mixture of complex signals into Independent Components by a complex version of the infomax ICA algorithm. Some results from a visual spatial selective attention experiment illustrate the differences between real time-domain ICA and complex spectral-domain ICA, and highlight properties of the obtained complex Independent Components.
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imaging brain dynamics using Independent Component Analysis
Proceedings of the IEEE, 2001Co-Authors: Tzyyping Jung, Scott Makeig, Martin J Mckeown, Anthony J Bell, Terrence J SejnowskiAbstract:The Analysis of electroencephalographic and magnetoencephalographic recordings is important both for basic brain research and for medical diagnosis and treatment. Independent Component Analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain.
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Independent Component Analysis of electroencephalographic data
Neural Information Processing Systems, 1995Co-Authors: Scott Makeig, Tzyyping Jung, Anthony J Bell, Terrence J SejnowskiAbstract:Because of the distance between the skull and brain and their different resistivities, electroencephalographic (EEG) data collected from any point on the human scalp includes activity generated within a large brain area. This spatial smearing of EEG data by volume conduction does not involve significant time delays, however, suggesting that the Independent Component Analysis (ICA) algorithm of Bell and Sejnowski [1] is suitable for performing blind source separation on EEG data. The ICA algorithm separates the problem of source identification from that of source localization. First results of applying the ICA algorithm to EEG and event-related potential (ERP) data collected during a sustained auditory detection task show: (1) ICA training is insensitive to different random seeds. (2) ICA may be used to segregate obvious artifactual EEG Components (line and muscle noise, eye movements) from other sources. (3) ICA is capable of isolating overlapping EEG phenomena, including alpha and theta bursts and spatially-separable ERP Components, to separate ICA channels. (4) Nonstationarities in EEG and behavioral state can be tracked using ICA via changes in the amount of residual correlation between ICA-filtered output channels.
