roiextract 0.0.2

Data-independent and data-driven optimization of extraction of ROI time series based on M/EEG

ROIextract

Optimization of extraction of ROI time series based on the cross-talk function (CTF) or source reconstruction of spatial patterns (REC). Work in progress!

Usage

Obtain a spatial filter that optimizes CTF properties:

from roiextract import ctf_optimize_label

sf = ctf_optimize_label(fwd, label, template, lambda_)

sf, props = ctf_optimize_label(fwd, label, template, lambda_, quantify=True)

sf = ctf_optimize_label(fwd, label, template, lambda_='auto', threshold=0.95)

Plot the filter as a topomap:

sf.plot(info)

Apply it to the data to obtain the time course of activity in the ROI/label:

label_tc = sf.apply(data)

Estimate the CTF for the filter:

ctf = sf.get_ctf_fwd(fwd)  # ctf is an instance of mne.SourceEstimate

Plot the CTF on the brain surface:

ctf.plot()