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unknown title

by Vesa Kiviniemi, Juha-heikki Kantola, A Jukka Jauhiainen, Aapo Hyvärinen, Osmo Tervonen A , 2003
"... www.elsevier.com/locate/ynimg Independent component analysis of nondeterministic fMRI signal sources ..."
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www.elsevier.com/locate/ynimg Independent component analysis of nondeterministic fMRI signal sources

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

by Arnaud Delorme, Scott Makeig - J. Neurosci. Methods
"... Abstract: We have developed a toolbox and graphic user interface, EEGLAB, running under the cross-platform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event i ..."
Abstract - Cited by 886 (45 self) - Add to MetaCart
information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decompositions including channel

Analysis of fMRI Data by Blind Separation Into Independent Spatial Components

by Martin J. Mckeown, Scott Makeig, Greg G. Brown, Tzyy-ping Jung, Sandra S. Kindermann, Anthony J. Bell, Terrence J. Sejnowski - HUMAN BRAIN MAPPING 6:160–188(1998) , 1998
"... Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent comp ..."
Abstract - Cited by 317 (18 self) - Add to MetaCart
component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129–1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson word/number task, and control tasks into spatially independent components. Each component consisted

Blind Signal Separation: Statistical Principles

by Jean-Francois Cardoso , 2003
"... Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mut ..."
Abstract - Cited by 529 (4 self) - Add to MetaCart
Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption

Multidimensional Independent Component Analysis.

by Jean-François Cardoso - In Proc. Int. Workshop on Higher-Order Stat , 1998
"... This discussion paper proposes to generalize the notion of Independent Component Analysis (ICA) to the notion of Multidimensional Independent Component Analysis (MICA). We start from the ICA or blind source separation (BSS) model and show that it can be uniquely identified provided it is properly p ..."
Abstract - Cited by 257 (15 self) - Add to MetaCart
This discussion paper proposes to generalize the notion of Independent Component Analysis (ICA) to the notion of Multidimensional Independent Component Analysis (MICA). We start from the ICA or blind source separation (BSS) model and show that it can be uniquely identified provided it is properly

Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources

by Te-won Lee, Mark Girolami, Terrence J. Sejnowski , 1999
"... An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a proj ..."
Abstract - Cited by 314 (22 self) - Add to MetaCart
An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a

Robust Independent Component Analysis for fMRI

by Ping Bai, Haipeng Shen, Young Truong , 2006
"... Summary. Independent component analysis (ICA) is an effective exploratory tool for analyzing spatio-temporal data. It has been successfully applied in analyzing functional Magnetic Resonance Imaging (fMRI) data, to recover the interested source signals from different parts of the brain. Due to the h ..."
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Summary. Independent component analysis (ICA) is an effective exploratory tool for analyzing spatio-temporal data. It has been successfully applied in analyzing functional Magnetic Resonance Imaging (fMRI) data, to recover the interested source signals from different parts of the brain. Due

Learning Overcomplete Representations

by Michael S. Lewicki, Terrence J. Sejnowski , 2000
"... In an overcomplete basis, the number of basis vectors is greater than the dimensionality of the input, and the representation of an input is not a unique combination of basis vectors. Overcomplete representations have been advocated because they have greater robustness in the presence of noise, can ..."
Abstract - Cited by 354 (10 self) - Add to MetaCart
efficiency. This can be viewed as a generalization of the technique of independent component analysis and provides a method for Bayesian reconstruction of signals in the presence of noise and for blind source separation when there are more sources than mixtures.

Blind source separation of multiple signal sources of fMRI data sets using independent component analysis

by B. B. Biswal, J. L. Ulmer - J. Comput. Assist. Tomogr , 1999
"... Purpose. As we seek to establish the use of fMRI as the method of choice for studying systems-level neuroscience, it is essential that we be able to distinguish the various “signal ” sources from the “noise ” sources in which they are immersed. Almost all published fMRI studies thus far have been us ..."
Abstract - Cited by 37 (0 self) - Add to MetaCart
Purpose. As we seek to establish the use of fMRI as the method of choice for studying systems-level neuroscience, it is essential that we be able to distinguish the various “signal ” sources from the “noise ” sources in which they are immersed. Almost all published fMRI studies thus far have been

Spatial, Temporal, and Spatiotemporal Independent Component Analysis of fMRI Data

by J.V. Stone, Jv Stone, J. Porrill, C. Buchel, K. Friston
"... Introduction The fMRI signal associated with a given voxel is affected by a subject's general arousal levels, the experimental task being executed, drifting sensor outputs, and noise. Thus, the signal at each voxel consists of a mixture of underlying source signals. One method for separating s ..."
Abstract - Cited by 14 (2 self) - Add to MetaCart
Introduction The fMRI signal associated with a given voxel is affected by a subject's general arousal levels, the experimental task being executed, drifting sensor outputs, and noise. Thus, the signal at each voxel consists of a mixture of underlying source signals. One method for separating
Results 1 - 10 of 960