[FieldTrip] NAI on trial-by-trail power estimates
stephen.whitmarsh at gmail.com
Fri Oct 13 17:29:35 CEST 2017
I am trying to optimize my beamformer power estimates, which are not bad,
but I want to see if I can improve them.
I do not seem to have a depth-bias, and am testing within-subject
between-condition contrasts, for which one does not need to correct for
noise differences. However, my analysis does involve trial-by-trial
analysis, and might be susceptible to noise differences over time.
I was wondering whether it would make sense to have trial-by-trial
corrections using the Neural Activation Index (NAI), as described in the
beamformer tutorial, i.e. by dividing my single-trial power estimates by
single-trial noise estimates.
Has anyone tried using either the NAI on a trial-by-trial basis?
Secondly, does this even make sense to you?
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