Model application

This part of the guide explains how the inverse model and the fsaverage source space morphing is applied. For previous steps, see MEG recording agnostic model computation, MEG recording agnostic model computation, MEG recording specific model computation.

Herein, the individual commands of this section will be explained in a step-by-step fashion.

The first step is to employ the previously created inverse model (see MEG recording specific model computation) to transform the data from sensor to source space.

src_data = finnpy.src_rec.inv_mdl.apply(sen_data, inv_mdl)

Afterwards, data is moved from subject specific into fs average space.

fsavg_src_data = finnpy.src_rec.subj_to_vsavg.apply(subj_to_fsavg_mdl, src_data)

Finally, the in fs-average space defined Desikan-Killiany is employed to consolidate individiual source space channels into regions.

(clust_src_data, chs, ch_names) = finnpy.src_rec.avg_src_reg.run(fsavg_src_data, subj_to_fsavg_mdl, fs_path)

This concludes the source construction pipeline. Potential pitfalls during source reconstruction are discussed in Likely pitfalls.