Likely pitfalls

This guide explains how to deal with likely pitfalls in source reconstruction for MEG using FiNNPy.

MEG and MRI coregistration

The coregistration between MEG and MRI space has been left unchecked. This step must be manually verified. This may be done as follows:

rec_meta_info = mne.io.read_info(data_path)
meg_ref_pts = finnpy.src_rec.coreg.load_meg_ref_pts(rec_meta_info)
(coreg, bad_hsp_pts) = finnpy.src_rec.coreg.run(subj_name, anatomy_path, rec_meta_info)
finnpy.src_rec.coreg.plot_coregistration(coreg, meg_ref_pts, bad_hsp_pts, anatomy_path, subj_name)

Producing the following output:

Sensor noise covariance

The sensor noise covariance is faulty. This may be investigated by adding a power spike to a sensor-space channel and investigate where it is projected.

Alternatively, multiple files for a sensor noise covariance may be acquired and their similarity compared.

Improper skull model

The skull model was improperly extracted. This step must be manually verified. This may be done as follows:

skull_skin_mdl = finnpy.src_rec.skull_skin_mdls.read(anatomy_path, subj_name, "MEG")
finnpy.src_rec.skull_skin_mdls.plot(skull_skin_mdl, anatomy_path, subj_name)

Producing the following output: