[Mne_analysis] Source Space Decoding Classification Timecourse

[Ghuman, Avniel]

Hi Cody,

Do you have the same number of trials in each condition after any trial rejection you do? If not, then the issue might be that 50% is not the correct chance level to think about, rather the correct chance level is the proportion of trials that is in your more frequent condition (eyeballing, maybe like 55%?). There are unbiased classifiers you can use, but I am not sure if they are built into MNE python...

Best wishes,
Avniel

________________________________
From: mne_analysis-bounces at nmr.mgh.harvard.edu [mne_analysis-bounces at nmr.mgh.harvard.edu] on behalf of Cushing, Cody [CCUSHING1 at mgh.harvard.edu]
Sent: Friday, August 04, 2017 5:11 PM
To: mne_analysis at nmr.mgh.harvard.edu
Subject: [Mne_analysis] Source Space Decoding Classification Timecourse

Hi,

I've been trying to modify the following example:

http://martinos.org/mne/dev/auto_examples/decoding/plot_decoding_spatio_temporal_source.html

to yield a time resolved classification accuracy.  I'm new to decoding so I've done it in a fairly brute way (just iterating this script over every time point), which yields a fairly convincing classification accuracy timecourse.  However, I'm a bit concerned at how high the accuracy is during the baseline, pre-stim period.  See attached for the modified script using the sample data and an example of the output.  I'm new to decoding, but the best answer I've been able to find for abnormally high pre-stim accuracy is failing to cross validate, but that shouldn't be the case as cross validation is being performed (but perhaps I'm doing it wrong) .  Is there something improper about my strategy here?  Thanks for any input.

Cheers,
Cody