Model Application

This part of the guide explains how the inverse model and the fsaverage source space morphing is applied. For previous steps, see Anatomy Extraction, Sensor Position Dependent Factors, Downstream Processing.

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 Downstream Processing) to transform the data from sensor to source space.

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

Afterwards, data are 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 individual source space channels into cortical 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.