EEG Sequence Imaging: A Markov Prior for the Variational Garrote

Sofie Therese Hansen; Lars Kai Hansen

EEG Sequence Imaging: A Markov Prior for the Variational Garrote

Sofie Therese Hansen
, Lars Kai Hansen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

We propose the following generalization of the Variational Garrote for sequential EEG imaging: A Markov prior to promote sparse, but temporally smooth source dynamics. We derive a set of modied Variational Garrote updates and analyze the role of the prior's hyperparameters. An experimental evaluation is given in simulated data and in a benchmark EEG data set.

Original language	English
Title of host publication	Proceedings of the 3rd NIPS Workshop on Machine Learning and Interpretation in Neuroimaging 2013
Number of pages	6
Publication date	2013
Publication status	Published - 2013
Event	27th Annual Conference on Neural Information Processing Systems (NIPS 2013) - Lake Tahoe, Nevada, United States Duration: 5 Dec 2013 → 10 Dec 2013 http://nips.cc/Conferences/2013/

Conference

Conference	27th Annual Conference on Neural Information Processing Systems (NIPS 2013)
Country/Territory	United States
City	Lake Tahoe, Nevada
Period	05/12/2013 → 10/12/2013
Internet address	http://nips.cc/Conferences/2013/

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title = "EEG Sequence Imaging: A Markov Prior for the Variational Garrote",

abstract = "We propose the following generalization of the Variational Garrote for sequential EEG imaging: A Markov prior to promote sparse, but temporally smooth source dynamics. We derive a set of modied Variational Garrote updates and analyze the role of the prior's hyperparameters. An experimental evaluation is given in simulated data and in a benchmark EEG data set.",

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note = "27th Annual Conference on Neural Information Processing Systems (NIPS 2013) ; Conference date: 05-12-2013 Through 10-12-2013",

url = "http://nips.cc/Conferences/2013/",

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AU - Hansen, Lars Kai

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N2 - We propose the following generalization of the Variational Garrote for sequential EEG imaging: A Markov prior to promote sparse, but temporally smooth source dynamics. We derive a set of modied Variational Garrote updates and analyze the role of the prior's hyperparameters. An experimental evaluation is given in simulated data and in a benchmark EEG data set.

AB - We propose the following generalization of the Variational Garrote for sequential EEG imaging: A Markov prior to promote sparse, but temporally smooth source dynamics. We derive a set of modied Variational Garrote updates and analyze the role of the prior's hyperparameters. An experimental evaluation is given in simulated data and in a benchmark EEG data set.

M3 - Article in proceedings

BT - Proceedings of the 3rd NIPS Workshop on Machine Learning and Interpretation in Neuroimaging 2013

T2 - 27th Annual Conference on Neural Information Processing Systems (NIPS 2013)

Y2 - 5 December 2013 through 10 December 2013

ER -

EEG Sequence Imaging: A Markov Prior for the Variational Garrote

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