Hierarchical multiscale Bayesian algorithms for biomagnetic brain imaging

We present novel hierarchical multiscale Bayesian algorithms for electromagnetic brain imaging using magnetoencephalography (MEG) and electroencephalography (EEG). We define sensor data measurements using a generative probabilistic graphical model that is hierarchical across spatial scales of brain regions and voxels. We then derive Bayesian algorithms for probabilistic inference with this graphical model. Performance of algorithm shows superiority to standard benchmark algorithms and more novel algorithms both in simulations and with real data.