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.
|Title of host publication||2013 International Winter Workshop on Brain-Computer Interface (BCI)|
|Publication status||Published - 2013|
|Event||2013 International Winter Workshop on Brain-Computer Interface (BCI) - Gangwo, Korea, Republic of|
Duration: 18 Feb 2013 → 20 Feb 2013
|Conference||2013 International Winter Workshop on Brain-Computer Interface (BCI)|
|Country||Korea, Republic of|
|Period||18/02/2013 → 20/02/2013|