Groupwise sparsity enforcing estimators for solving the EEG/MEG inverse problem
published: Dec. 18, 2008, recorded: December 2008, views: 344
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Cerebral current ﬂows are directly related to information transfer in the brain and thus an excellent means for studying the mechanisms of cogni- tive processing. Electro- and Magneto-encephalography, EEG and MEG, are noninvasive measures of these electric currents (EEG) or their respec- tive accompanying magnetic ﬁelds (MEG). The reconstruction of the cere- bral current density from EEG/MEG measurements is an ill-posed inverse problem. As the forward mapping from the current sources to the external sensors is linear, the inverse problem may be formulated as a highly un- der determined linear system of equations, which has no unique solution. The common strategy to deal with this ambiguity is regularization, i.e. ﬁt- ting the data with an additional penalization of the sources. Both l2-norm and l1-norm based penalties have been proposed based on the neurophys- iologically motivated assumptions of smoothness and sparsity, respectively.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !