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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: Langs, G.; Rish, I.; Grosse-Wentrup, M. (ed.), Machine learning and interpretation in neuroimaging: Conference proceedings: Revised selected and invited contributions, pp. 148-155
Publisher: Berlin : Springer
ISBN: 9783642347139
DOI: https://doi.org/10.1007/978-3-642-34713-9_19
Annotation: International Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2011) (Sierra Nevada, Spain, December 16-17, 2011)
Publication type: Article in monograph or in proceedings

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

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Editor(s): Langs, G.; Rish, I.; Grosse-Wentrup, M.; Murphy, B.
Subject: 120 000 Neuronal Coherence
150 000 MR Techniques in Brain Function
160 000 Neuronal Oscillations
Cognitive artificial intelligence
DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication
Data Science
Organization: Data Science
PI Group Neuronal Oscillations
SW OZ DCC AI
PI Group Neurobiology of Language
Book title: Langs, G.; Rish, I.; Grosse-Wentrup, M. (ed.), Machine learning and interpretation in neuroimaging: Conference proceedings: Revised selected and invited contributions
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
Languages used: English (eng)

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