Mobile real-time EEG imaging Bayesian inference with sparse, temporally smooth source priors

Lars Kai Hansen, Sofie Therese Hansen, Carsten Stahlhut

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Abstract

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.
Original languageEnglish
Title of host publication2013 International Winter Workshop on Brain-Computer Interface (BCI)
PublisherIEEE
Publication date2013
Pages6-7
ISBN (Print)978-1-4673-5974-0
DOIs
Publication statusPublished - 2013
Event2013 International Winter Workshop on Brain-Computer Interface (BCI) - Gangwo, Korea, Republic of
Duration: 18 Feb 201320 Feb 2013
https://brain.korea.ac.kr/bci2013/

Conference

Conference2013 International Winter Workshop on Brain-Computer Interface (BCI)
CountryKorea, Republic of
CityGangwo
Period18/02/201320/02/2013
Internet address

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