SigProC and CBU
In this Wiki, we collect material that may be relevant for the possible collaboration between SigProC and CBU. If you want to have something added here, but you don't have rights to modify the page, please send your suggestion to Olaf or Matti.
17 Oct: Added Sections "Non-linear Estimation", "Kalman Filter in Brain Imaging", "Gaussian Processes (Gaussian random fields)" that were sent by Simo.
General EEG/MEG papers
- M. Hämäläinen, R. Hari, R. Ilmoniemi, J. Knuutila, and O. V. Lounasmaa, "Magnetoencephalography - theory, instrumentation, and applications to noninvasive studies of the working human brain," Reviews of Modern Physics, vol. 65, pp. 413-497, 1993.
- S. Baillet, J. C. Mosher, and R. M. Leahy, "Electromagnetic Brain Mapping," IEEE Signal Processing Magazine, vol. 18, pp. 14 - 30, 2001.
Inverse problem of EEG/MEG
Evaluating and comparing different inversion methods:
O. Hauk, D.G. Wakeman, R. Henson (2011). Comparison of noise-normalized minimum norm estimates for MEG analysis using multiple resolution metrics, NeuroImage 54(3), 1966-1974.
Basics of distributed approaches to the EEG/MEG inverse problem:
Hauk, O. (2004). Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data. Neuroimage, 21(4), 1612-1621.
Geometrical description of fMRI-weighted minimum norm estimation:
Ahlfors, S. P., & Simpson, G. V. (2004). Geometrical interpretation of fMRI-guided MEG/EEG inverse estimates. Neuroimage, 22(1), 323-332.
Bayesian multi-dipole fitting by Sorrentino et al.
Previous work done at the BECS, Aalto University:
Thesis "Hierarchical Bayesian Aspects of Distributed Neuromagnetic Source Models" by Aapo Nummenmaa
Thesis "Computational Methods for Bayesian Estimation of Neuromagnetic Sources" by Toni Auranen
Basic physics and forward modeling
Thesis "Boundary Element Method in Spatial Characterization of the Electrocardiogram" by Matti Stenroos (this is about electrocardiography, but the physics and the forward solution is in principle the same as in EEG/MEG)
- Review of our Parametric Empirical Bayesian (Gaussian process modelling) efforts for combining fMRI, EEG, MEG
- Recent review of Dynamic Causal Modelling (DCM), particularly for experimental perturbations
- Recent application of DCM to discovering networks in endogenous (eg resting state) data
Review of pattern classification approaches in neuroimaging:
Kriegeskorte, N. Pattern-information analysis: from stimulus decoding to computational-model testing. Neuroimage, 56(2), 411-421.
Material on non-linear dynamic Bayesian estimation, especially particle filtering:
Simo's course material on non-linear (Kalman) filtering and smoothing:
Simo's thesis on modeling with SDEs and Bayesian estimation of non-linear dynamic systems:
Kalman Filter in Brain Imaging
The extended Kalman filter was used in brain imaging e.g. here:
Kalman filter's for the EEG(MEG) inverse problem:
A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering. by Galka et al., Neuroimage 2004
Recursive penalized least squares solution for dynamical inverse problems of EEG generation by Yamashita et al., HBM 2004.
A Bayesian perspective on Generalised Filtering by Friston et al.
Gaussian processes (Gaussian random fields)
Online material on Gaussian process based machine learning can be found here:
Regarding inference on spatio-temporal Gaussian processes, SPDEs and such, something can be found here: