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

• Main Authors: Hansen, L. K., Hansen, S. T., Stahlhut, C.
• Format: Conference Proceeding
• Language: English
• Subjects: Bayes methods; Brain modeling; EEG; Electroencephalography; ill-posed inverse; Imaging; Monitoring; real-time imaging; Real-time systems; Scalp; temporal sparsity promoting prior
• DOI: 10.1109/IWW-BCI.2013.6506608

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.