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From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition

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Title: From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition
Authors: Studer, Daniel1 daniel.studer@puk.unibe.ch, Hoffmann, Ulrich2 ulrich.hoffmann@epfl.ch, Koenig, Thomas1 thomas.koenig@puk.unibe.ch
Source: Journal of Neuroscience Methods. Jun2006, Vol. 153 Issue 2, p261-275. 15p.
Subjects: Electroencephalography, Algorithms, Electrodes, Distribution (Probability theory)
Abstract: Abstract: In this work, we present a multichannel EEG decomposition model based on an adaptive topographic time–frequency approximation technique. It is an extension of the Matching Pursuit algorithm and called dependency multichannel matching pursuit (DMMP). It takes the physiologically explainable and statistically observable topographic dependencies between the channels into account, namely the spatial smoothness of neighboring electrodes that is implied by the electric leadfield. DMMP decomposes a multichannel signal as a weighted sum of atoms from a given dictionary where the single channels are represented from exactly the same subset of a complete dictionary. The decomposition is illustrated on topographical EEG data during different physiological conditions using a complete Gabor dictionary. Further the extension of the single-channel time–frequency distribution to a multichannel time–frequency distribution is given. This can be used for the visualization of the decomposition structure of multichannel EEG. A clustering procedure applied to the topographies, the vectors of the corresponding contribution of an atom to the signal in each channel produced by DMMP, leads to an extremely sparse topographic decomposition of the EEG. [Copyright &y& Elsevier]
Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition
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  Data: <searchLink fieldCode="AR" term="%22Studer%2C+Daniel%22">Studer, Daniel</searchLink><relatesTo>1</relatesTo><i> daniel.studer@puk.unibe.ch</i><br /><searchLink fieldCode="AR" term="%22Hoffmann%2C+Ulrich%22">Hoffmann, Ulrich</searchLink><relatesTo>2</relatesTo><i> ulrich.hoffmann@epfl.ch</i><br /><searchLink fieldCode="AR" term="%22Koenig%2C+Thomas%22">Koenig, Thomas</searchLink><relatesTo>1</relatesTo><i> thomas.koenig@puk.unibe.ch</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Neuroscience+Methods%22">Journal of Neuroscience Methods</searchLink>. Jun2006, Vol. 153 Issue 2, p261-275. 15p.
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  Data: Abstract: In this work, we present a multichannel EEG decomposition model based on an adaptive topographic time–frequency approximation technique. It is an extension of the Matching Pursuit algorithm and called dependency multichannel matching pursuit (DMMP). It takes the physiologically explainable and statistically observable topographic dependencies between the channels into account, namely the spatial smoothness of neighboring electrodes that is implied by the electric leadfield. DMMP decomposes a multichannel signal as a weighted sum of atoms from a given dictionary where the single channels are represented from exactly the same subset of a complete dictionary. The decomposition is illustrated on topographical EEG data during different physiological conditions using a complete Gabor dictionary. Further the extension of the single-channel time–frequency distribution to a multichannel time–frequency distribution is given. This can be used for the visualization of the decomposition structure of multichannel EEG. A clustering procedure applied to the topographies, the vectors of the corresponding contribution of an atom to the signal in each channel produced by DMMP, leads to an extremely sparse topographic decomposition of the EEG. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Algorithms
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      – SubjectFull: Electrodes
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      – SubjectFull: Distribution (Probability theory)
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              Text: Jun2006
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