Recursive penalized least squares solution for dynamical inverse problems of EEG generation

Okito Yamashita, Andreas Galka, Tohru Ozaki, Rolando Biscay, Pedro Valdes-Sosa
2004 Human Brain Mapping  
In the dynamical inverse problem of electroencephalogram (EEG) generation where a specific dynamics for the electrical current distribution is assumed, we can impose general spatiotemporal constraints onto the solution by casting the problem into a state space representation and assuming a specific class of parametric models for the dynamics. The Akaike Bayesian Information Criterion (ABIC), which is based on the Type II likelihood, was used to estimate the parameters and evaluate the model. In
more » ... addition, dynamic low-resolution brain electromagnetic tomography (LORETA), a new approach for estimating the current distribution is introduced. A recursive penalized least squares (RPLS) step forms the main element of our implementation. To obtain improved inverse solutions, dynamic LORETA exploits both spatial and temporal information, whereas LORETA uses only spatial information. A considerable improvement in performance compared to LORETA was found when dynamic LORETA was applied to simulated EEG data, and the new method was applied also to clinical EEG data. Hum. Brain Mapp. 21:221-235, 2004. The inverse problem is defined the estimation of current density J from given measurement Y and constitutes an ill-posed problem, because the number of scalp electrodes is much smaller than the number of voxels for which the ᭜ Yamashita et al. ᭜ ᭜ 222 ᭜ ᭜ RPLS Solution for Dynamical Inverse Problems ᭜ ᭜ 223 ᭜ ᭜ RPLS Solution for Dynamical Inverse Problems ᭜ ᭜ 227 ᭜ ᭜ RPLS Solution for Dynamical Inverse Problems ᭜ ᭜ 229 ᭜ ᭜ RPLS Solution for Dynamical Inverse Problems ᭜ ᭜ 231 ᭜ ᭜ RPLS Solution for Dynamical Inverse Problems ᭜ ᭜ 235 ᭜
doi:10.1002/hbm.20000 pmid:15038004 fatcat:xdoakukw7rfrlfoujwz6uqopqq