[Homer-users] Short Separation GLM without Block Avg

[Friedman,Leah]

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Hello Homer users,

I had some questions regarding short-separation methods and the GLM function (hmrDeconvHRF_DriftSS.m). This function seems to be the primary method of using short-separation channel regression. However, it seems to require knowledge of experimental stimuli and has a built-in block average. We’ve watched a number of homer tutorials and training sessions and still have the following questions regarding these methods:


  1.  We would like to be able to use short-separation channel regression and get the data for the entire sample rather than a block average. Is there a different function to do this or a way to modify hmrDeconvHRF_DriftSS.m to do this?
  2.  How is the stimulus vector input used in hmrDeconvHRF_DriftSS.m and is it possible use the function without providing information about the stimuli?
  3.  If we chose not to neutralize motion artifacts in hmrDeconvHRF_DriftSS.m, would it be appropriate to implement a different motion artifact removal method prior to hmrDeconvHRF_DriftSS.m? Or would the motion artifact removal go after hmrDeconvHRF_DriftSS.m in the processing pipeline?

We have looked at using hmrDeconvHRF_DriftSS.m both within the GUI and as a standalone function outside of the GUI.

Thanks in advance for any responses (partial or complete)!

Sincerely,
Leah Friedman

—
Drexel University, 2019
B.S. Cognitive Neuroscience
lmf323 at drexel.edu<mailto:lmf323 at drexel.edu>
Drexel AIR Lab<http://www.drexelairlab.com/>
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