An Alternative Perspective on Adaptive Independent Component Analysis Algorithms

Girolami, Mark

doi:10.1162/089976698300016981

Cited by 100 publications

(51 citation statements)

References 22 publications

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“…A symmetric strictly subgaussian density can be modeled using a symmetrical form of the Pearson mixture model (Pearson, 1894) as follows (Girolami, 1997(Girolami, , 1998: 12) where N(µ, σ 2 ) is the normal density with mean µ and variance σ 2 . Figure 1 shows the form of the density p(u) for σ 2 = 1 with varying µ = [0 · · · 2].…”

Section: Deriving a Learning Rule To Separate Sub-and Supergaussianmentioning

confidence: 99%

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

1999

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An extension of the infomax algorithm of Bell and Sejnowski (1995) ispresented 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 projection pursuit index. Parameterized probability distributions that have sub-and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub-and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.

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Section: Deriving a Learning Rule To Separate Sub-and Supergaussianmentioning

confidence: 99%

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

1999

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“…However, this study did not attempt to remove the identi®ed artifacts. Jung et al (1998aJung et al ( ,b, 2000 introduced an ICAbased method based on an extended infomax ICA algorithm (Girolami, 1998;Lee et al, 1999). This method can be used to detect and remove a wide variety of artifacts (including eye blinks, muscle noise, heart signal, and line noise) from spontaneous EEG data.…”

Section: Introductionmentioning

confidence: 99%

Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects

Jung¹,

Makeig²,

Westerfield³

et al. 2000

Clinical Neurophysiology

1,213

721

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Objectives: Electrical potentials produced by blinks and eye movements present serious problems for electroencephalographic (EEG) and event-related potential (ERP) data interpretation and analysis, particularly for analysis of data from some clinical populations. Often, all epochs contaminated by large eye artifacts are rejected as unusable, though this may prove unacceptable when blinks and eye movements occur frequently.Methods: Frontal channels are often used as reference signals to regress out eye artifacts, but inevitably portions of relevant EEG signals also appearing in EOG channels are thereby eliminated or mixed into other scalp channels. A generally applicable adaptive method for removing artifacts from EEG records based on blind source separation by independent component analysis (ICA) (Neural Computation 7 (1995) 1129; Neural Computation 10(8) (1998) 2103; Neural Computation 11(2) (1999) 606) overcomes these limitations.Results: Results on EEG data collected from 28 normal controls and 22 clinical subjects performing a visual selective attention task show that ICA can be used to effectively detect, separate and remove ocular artifacts from even strongly contaminated EEG recordings. The results compare favorably to those obtained using rejection or regression methods.Conclusions: The ICA method can preserve ERP contributions from all of the recorded trials and all the recorded data channels, even when none of the single trials are artifact-free. q

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“…Any sub-Gaussian prior will suffice to extract sub-Gaussian independent components. This conjecture also leads to the generally successful 'extended ICA' algorithms (Girolami, 1998;Lee, Girolami & Sejnowsk, 1999) that switch the component priors, ( )…”

Section: Assessment Methodsmentioning

confidence: 95%

Operator Functional State Assessment (L'evaluation de l'aptitude operationnelle de l'operateur humain)

2004

PsycEXTRA Dataset

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An Alternative Perspective on Adaptive Independent Component Analysis Algorithms

Cited by 100 publications

References 22 publications

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

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

Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects

Operator Functional State Assessment (L'evaluation de l'aptitude operationnelle de l'operateur humain)

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