| import openmeeg as om |
| ## Load data |
| geom = om.Geometry() |
| geom.read(‘head_model.geom’, ‘head_model.cond’) |
| dipoles = om.Matrix() |
| dipoles.load(‘cortex_dipoles.txt’) |
| meg_sensors = om.Sensors() |
| meg_sensors.load(‘meg_channels.squids’) |
| eeg_electrodes = om.Matrix() |
| eeg_electrodes.load(‘eeg_channels.txt’) |
| int_electrodes = om.Matrix() |
| int_electrodes.load(‘internal_electrodes.txt’) |
| ## Assemble matrices |
| gauss_order = 3 |
| use_adaptive_integration = True |
| hm = om.HeadMat(geom, gauss_order) |
| hminv = hm.inverse() |
| dsm = om.DipSourceMat(geom, dipoles, gauss_order, use_adaptive_integration) |
| # For EEG |
| h2em = om.Head2EEGMat(geom, eeg_electrodes) |
| # For MEG |
| ds2mm = om.DipSource2MEGMat(dipoles, meg_sensors) |
| h2mm = om.Head2MEGMat(geom, meg_sensors) |
| # For EIT (using the same electrodes as EEG) |
| eitsm = om.EITSourceMat(geom, eeg_electrodes, gauss_order) |
| # For Internal Potential |
| iphm = om.Surf2VolMat(geom, int_electrodes) |
| ipsm = om.DipSource2InternalPotMat(geom, dipoles, int_electrodes) |
| ## Compute leadfiels |
| eeg_leadfield = om.GainEEG(hminv, dsm, h2em) |
| meg_leadfield = om.GainMEG(hminv, dsm, h2mm, ds2mm) |
| eit_leadfield = om.GainEEG(hminv, eitsm, h2em) |
| ip_leadfield = om.GainInternalPot(hminv, dsm, iphm, ipsm) |
