Content
- common inverse problems in biomedicine, in particular neuroimaging
- physical foundations of magneto-/electroencephalography (M/EEG)
- forward modeling and physics simulation for M/EEG
- dipole fits, beamforming, scanning techniques
- penalized likelihood approach: smoothness, structured sparsity, elastic net, total variation denoising
- Bayesian inference: maximum a-posteriori estimation, hierarchical and empirical Bayes, sparse Bayesian learning
- noise learning approaches
- blind source separation as a statistical inverse problem
- simulations and validation
- research software
- applications in brain-computer interfacing and neurology