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Anthony J. Bell
Researcher at Salk Institute for Biological Studies
Publications - 28
Citations - 20078
Anthony J. Bell is an academic researcher from Salk Institute for Biological Studies. The author has contributed to research in topics: Independent component analysis & Infomax. The author has an hindex of 23, co-authored 28 publications receiving 18950 citations. Previous affiliations of Anthony J. Bell include Interval Research Corporation & University of California, San Diego.
Papers
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Journal ArticleDOI
An information-maximization approach to blind separation and blind deconvolution
TL;DR: It is suggested that information maximization provides a unifying framework for problems in "blind" signal processing and dependencies of information transfer on time delays are derived.
Journal ArticleDOI
The "independent components" of natural scenes are edge filters.
TL;DR: It is shown that a new unsupervised learning algorithm based on information maximization, a nonlinear "infomax" network, when applied to an ensemble of natural scenes produces sets of visual filters that are localized and oriented.
Journal ArticleDOI
Analysis of fMRI data by blind separation into independent spatial components
Martin J. McKeown,Scott Makeig,Greg G. Brown,Tzyy-Ping Jung,Sandra S. Kindermann,Anthony J. Bell,Terrence J. Sejnowski,Terrence J. Sejnowski +7 more
TL;DR: This work 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, and found the ICA algorithm was superior to principal component analysis (PCA) in determining the spatial and temporal extent of task‐related activation.
Proceedings Article
Independent Component Analysis of Electroencephalographic Data
TL;DR: First results of applying the ICA algorithm to EEG and event-related potential (ERP) data collected during a sustained auditory detection task show that ICA training is insensitive to different random seeds and ICA may be used to segregate obvious artifactual EEG components from other sources.
Journal ArticleDOI
Blind separation of auditory event-related brain responses into independent components
TL;DR: This paper reports the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses.