Independent Component Analysis in Wave Decomposition of Auditory Evoked Fields

Vigário, Ricardo; Särelä, Jaakko; Oja, Erkki

doi:10.1007/978-1-4471-1599-1_41

Cited by 27 publications

(28 citation statements)

References 7 publications

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“…Experiments on different kinds of real life data have also been performed using the contrast functions and algorithms introduced above. These applications include artifact cancellation in EEG and MEG [36], [37], decomposition of evoked fields in MEG [38], and feature extraction of image data [25], [35]. These experiments further validate the ICA methods introduced in this paper.…”

Section: Simulation and Experimental Resultssupporting

confidence: 64%

“…Taking the instantaneous gradient of the approximation of negentropy in (7) with respect to , and taking the normalization into account, one obtains the following Hebbian-like learning rule: normalize (38) where . This is equivalent to the learning rule in [24], except that the self-adaptation constant is different.…”

Section: Appendix B Adaptive Neural Algorithmsmentioning

confidence: 99%

“…This is equivalent to the learning rule in [24], except that the self-adaptation constant is different. To find the whole -dimensional transform , one can then use a network of neurons, each of which learns according to (38). Of course, some kind of feedback is then necessary.…”

Section: Appendix B Adaptive Neural Algorithmsmentioning

confidence: 99%

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Fast and robust fixed-point algorithms for independent component analysis

Hyvärinen

1999

IEEE Trans. Neural Netw.

5,460

3,423

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Abstract-Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon's informationtheoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast (objective) functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions. These algorithms optimize the contrast functions very fast and reliably.

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Section: Simulation and Experimental Resultssupporting

confidence: 64%

Section: Appendix B Adaptive Neural Algorithmsmentioning

confidence: 99%

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Fast and robust fixed-point algorithms for independent component analysis

Hyvärinen

1999

IEEE Trans. Neural Netw.

5,460

3,423

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“…Ce dernier point est abordé dans [92] et [80] La séparation de potentiels évoqués, induits par des stimulations de natures différentes, a été considérée dans [123]. R. N. Vigario et al ont montré, en utilisant toujours l'algorithme développé dans [77], que les méthodes de SAS sont capables, à partir de stimulations somesthésiques et auditives simultanées, de différencier les réponses du cerveau à ces stimuli sur les enregistrements MEG.…”

Section: ) Les Algorithmes Icar Et Birthunclassified

Séparation aveugle de sources en ingénierie biomédicale

Kachenoura

Albera

Senhadji

2007

IRBM

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Blind Source Separation (BSS) now raises great interest. In fact, BSS plays an important role in many diverse application areas, including medical diagnosis (separation of electroencephalogram signals for instance).Consequently, the BSS problem is addressed in this paper through i) the description of mathematical tools, ii) the bibliographical survey of classical methods and biomedical applications, and iii) the detailed presentation of six BSS algorithms.

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“…One such technique that we have used is blind source separation (BSS). BSS has been shown to segregate neuronal from nonneuronal signals, and neuronal signals from each other, in both EEG [Jung et al, 2000a, b;Makeig et al, 1997Makeig et al, , 1999 and MEG [Cao et al, 2000;Tang et al, 2000aTang et al, ,b, 2002Tang and Pearlmutter, 2003;Vigário et al, 1998Vigário et al, , 1999Vigário et al, , 2000Wü bbeler et al, 2000;Ziehe et al, 2000]. For these reasons, despite its limitations, it seems feasible to use the localizer proposed above as a stage in a practical robust real-time MEG processing pipeline.…”

Section: Figure 12mentioning

confidence: 99%

Fast robust subject‐independent magnetoencephalographic source localization using an artificial neural network

Jun

Pearlmutter

2004

Human Brain Mapping

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Abstract:We describe a system that localizes a single dipole to reasonable accuracy from noisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained to map sensor signals and head position to dipole location. Including head position overcomes the previous need to retrain the MLP for each subject and session. The training dataset was generated by mapping randomly chosen dipoles and head positions through an analytic model and adding noise from real MEG recordings. After training, a localization took 0.7 ms with an average error of 0.90 cm. A few iterations of a Levenberg-Marquardt routine using the MLP output as its initial guess took 15 ms and improved accuracy to 0.53 cm, which approaches the natural limit on accuracy imposed by noise. We applied these methods to localize single dipole sources from MEG components isolated by blind source separation and compared the estimated locations to those generated by standard manually assisted commercial software. Hum Brain Mapp 24:21-34, 2005.

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Independent Component Analysis in Wave Decomposition of Auditory Evoked Fields

Cited by 27 publications

References 7 publications

Fast and robust fixed-point algorithms for independent component analysis

Fast and robust fixed-point algorithms for independent component analysis

Séparation aveugle de sources en ingénierie biomédicale

Fast robust subject‐independent magnetoencephalographic source localization using an artificial neural network

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