Fulltext present in this item

Thumbnail
Fulltext: 100980.pdf
Format: PDF
Description: author's version
Title: The Dynamic Beamformer
Author(s): Bahramisharif, A. ; Gerven, M.A.J. van ; Schoffelen, J.M. ; Ghahramani, Z.; Heskes, T.M.
Publication year: 2012
In: Lecture Notes in Computer Science, (2012)Langs, G.; et al. (ed.), Machine Learning and Interpretation in Neuroimaging 2011, pp. 148-155
Publisher: Berlin/Heidelberg : Springer Verlag
ISBN: 9783642347139
ISSN: 0302-9743
Publication type: Article in monograph or in proceedings

Please use this identifier to cite or link to this item : http://hdl.handle.net/2066/100980   http://hdl.handle.net/2066/100980

Display more details
Editor(s): Langs, G.; et al.
Subject: Cognitive artificial intelligence
DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication
Organization: SW OZ DCC KI
Journal title:
Lecture Notes in Computer Science
Book title: Langs, G.; et al. (ed.), Machine Learning and Interpretation in Neuroimaging 2011
Page start: p. 148
Page end: p. 155
Abstract:
As the number of possible neural sources is much higher than the number of MEG or EEG sensor readings, the inverse problem of estimating source amplitudes from sensor readings has many solutions. A common approach to tackle this problem is to assume that all sources are independent from each other. This approach is widely used in the neuroscience community and is known as beamforming. Since the source amplitude is likely to change smoothly over time, we expect to improve the source localization by taking the temporal dynamics into account. In this paper, we incorporate the independence assumption of the standard beamformer in a linear dynamic system, and we show that by using the leadfield matrix as the observation model and setting the covariance of the observation noise to be proportional to the covariance of the observation, we arrive at the dynamic beamformer

This item appears in the following Collection(s)

Browse

Statistics