162013-03-08 10:02:47localhostconverted to 1.6 markup152012-04-16 16:53:27YaaraErez142010-07-22 15:34:10YaaraErez132010-06-17 11:28:32YaaraErez122010-06-17 11:27:05YaaraErez112010-06-15 14:37:56YaaraErez102010-06-10 13:12:36YaaraErez92010-06-10 13:04:51YaaraErez82010-06-10 12:06:33YaaraErez72010-06-09 16:40:49YaaraErez62010-06-09 15:58:46YaaraErez52010-06-09 15:57:30YaaraErez42010-06-09 15:57:10YaaraErez32010-06-09 15:53:09YaaraErez22010-06-09 15:52:20YaaraErez12010-06-09 15:45:06YaaraErezThe following script computes the inverse operator, which will later be applied to your MEG data to compute the source estimates. You must have created a forward solution already. Just as for the forward solution, two inverse operators will be created: One for MEG only, and one for combined EEG and MEG. If you only have MEG data, you can skip the latter. The main ingredients for this script are * the forward solution * the noise covariance matrix The end product will be the inverse operator (something ending in *-inv.fif), which can be read into Matlab using mne_read_inverse_operator. The inverse operators will be computed using the loose orientation constraint and without depth weighting (see MNE manual for details and more options). The parameters below are reasonable choices for standard analyses. However, these Wiki pages are not supposed to substitute the MNE manual, reading papers, and discussions with more experienced researchers. ' # root directory for your MEG data ]]>The --loose option requires dipoles to be almost perpendicular to the cortical surface, with some variation in the tangential plane (0.2 means that variation in the tangential dimension is one-fifth of the radial dimension). The --megreg/eegreg option are "regularization parameters" for the covariance matrices.