Conference paper
Mobile real-time EEG imaging Bayesian inference with sparse, temporally smooth source priors
EEG based real-time imaging of human brain function has many potential applications including quality control, in-line experimental design, brain state decoding, and neuro-feedback. In mobile applications these possibilities are attractive as elements in systems for personal state monitoring and well-being, and in clinical settings were patients may need imaging under quasi-natural conditions.
Challenges related to the ill-posed nature of the EEG imaging problem escalate in mobile real-time systems and new algorithms and the use of meta-data may be necessary to succeed. Based on recent work (Delorme et al., 2011) we hypothesize that solutions of interest are sparse. We propose a new Markovian prior for temporally sparse solutions and a direct search for sparse solutions as implemented by the so-called “variational garrote” (Kappen, 2011).
We show that the new prior and inference scheme leads to improved solutions over competing sparse Bayesian schemes based on the “multiple measurement vectors” approach.
Language: | English |
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Publisher: | IEEE |
Year: | 2013 |
Pages: | 6-7 |
Proceedings: | 2013 International Winter Workshop on Brain-Computer Interface (BCI) |
ISBN: | 1467359734 , 1467359742 , 9781467359733 and 9781467359740 |
Types: | Conference paper |
DOI: | 10.1109/IWW-BCI.2013.6506608 |
ORCIDs: | Hansen, Lars Kai and Hansen, Sofie Therese |