[Mne_analysis] extracting ROI sources using mne_compute_raw_inverse

[Matt Panichello]

Hi everyone,

I am trying extract the sources from a series of functional-anatomical rois
for frequency analysis, but am having some trouble getting good quality
data. I'm using mne_compute_raw_inverse to extract the data before loading
it into matlab.

Subjects completed an object recognition task during recording. To check the
quality of the extracted data, I've been averaging the timecourses in an
early visual ROI across all vertices and visualizing the evoked response.
For some of the subjects (e.g., S003-a, attached), the evoked response looks
normal. The evoked response for many subjects, however, looks like senseless
noise (S007-a) or shows an unexpected negative deflection (S010-a).

I don't think these problems are solely due to the labels I've drawn, or to
the intrinsic quality of the raw data. This is because when I average the
raw data for each subject using mne_process_raw, and then view the average
of the vertices inside the same ROIs using mne_analyze, the evoked responses
all look as expected, often very different from the averages produced from
my mne_compute_raw_inverse pipeline (see S007-b and S010-b).

Does anyone have an idea what the problem might be? Are there any special
considerations to take in account when using mne_compute_raw_inverse to
extract sources from ROIs? Why might the label averages using the two
different methods described look so different?

Thanks in advance for any help,

Matt
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