[Mne_analysis] identifying insensitive sensor time series for rejection

Talitha Ford tcford at swin.edu.au
Fri Jul 29 02:58:37 EDT 2016
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Hi Eric,
Thanks, I and my colleagues have had a good look through them and none seem to do what we need. The closest is http://mne-tools.github.io/stable/auto_tutorials/plot_stats_cluster_1samp_test_time_frequency.html though it looks at the TF. I have calculated the t stat for each time point from the evoked (with the standard deviations of the averaged evoked data) and plotted it on the time series which gives me a pretty good idea. Perhaps the request is a little specific to my data, although I would be happy to share what I have come up with.
Thank you anyway!

Best,
Talitha


On 29 Jul 2016, at 12:55, Eric Larson <larson.eric.d at gmail.com<mailto:larson.eric.d at gmail.com>> wrote:

is there a function/s to test whether a section of the averaged deviant time series from one channel (MEG1621 from 10oms to 200ms) is significantly greater than zero? The data is baseline corrected, so I presume against zero would be the correct comparison.

Can you take a look at the statistics examples and tutorials on the website to see if any of them cover your use case? If not, we should probably add one.

Also, I just wanted to let you know that subtracting one average() from another results in the average of the two rather than one minus the other. For example: dev_ave - stand_ave = (dev_ave - stand_ave)/2. I’m not sure if this is intentional, or an error in the code for average()?

This has come up multiple times before, so we are actually planning to change this behavior in the next MNE-Python release (0.13):

https://github.com/mne-tools/mne-python/pull/3400

For now, you can use `mne.combine_evoked` (with weights=[1, -1]) to get the plain subtraction you want.

Eric

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