An Information-Maximization Approach to Blind Separation and Blind Deconvolution
- A. J. Bell, T. Sejnowski
- Computer ScienceNeural Computation
- 1 November 1995
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.
Analysis of fMRI data by blind separation into independent spatial components
- Martin J. Mckeown, S. Makeig, T. Sejnowski
- Psychology, BiologyHuman Brain Mapping
- 1998
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.
The “independent components” of natural scenes are edge filters
- A. J. Bell, T. Sejnowski
- Computer ScienceVision Research
- 1 December 1997
Independent Component Analysis of Electroencephalographic Data
- S. Makeig, A. J. Bell, T. Jung, T. Sejnowski
- Computer ScienceNIPS
- 27 November 1995
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.
Blind separation of auditory event-related brain responses into independent components.
- S. Makeig, T. Jung, A. J. Bell, D. Ghahremani, T. Sejnowski
- Computer ScienceProceedings of the National Academy of Sciences…
- 30 September 1997
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.
Blind separation and blind deconvolution: an information-theoretic approach
- A. J. Bell, T. Sejnowski
- Computer ScienceIEEE International Conference on Acoustics…
- 9 May 1995
A new algorithm is derived and with it it perform nearly perfect separation of up to 10 digitally mixed human speakers, better performance than any previous algorithms for blind separation.
THE CO-INFORMATION LATTICE
- A. J. Bell
- Computer Science
- 2003
The co-information lattice sheds light on the problem of approximating a joint density with a set of marginal densities, though as usual the authors run into the partition function.
Imaging brain dynamics using independent component analysis
- T. Jung, S. Makeig, M. McKeown, A. J. Bell, Te-Won Lee, T. Sejnowski
- Computer ScienceProceedings of the IEEE
- 1 July 2001
The assumptions underlying ICA are outlined and its application to a variety of electrical and hemodynamic recordings from the human brain is demonstrated.
INDEPENDENT COMPONENT ANALYSIS OF BIOMEDICAL SIGNALS
- T. Jung, S. Makeig, T. Sejnowski
- Biology
- 2000
This research attacked the mode of reinforcement learning using a probabilistic approach and found it to be a very simple and efficient way to model the brain’s response to reinforcement learning.
Edges are the Independent Components of Natural Scenes
- A. J. Bell, T. Sejnowski
- BiologyNIPS
- 3 December 1996
It is shown here that non-linear 'infomax', when applied to an ensemble of natural scenes, produces sets of visual filters that are localised and oriented and resemble the receptive fields of simple cells in visual cortex, which suggests that these neurons form an information-theoretic co-ordinate system for images.
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