[Mne_analysis] Cluster-based Permutation T-test for Decoders

[JR KING]

        External Email - Use Caution        

You can use a one-versus-all classifier and compute the average AUC across
categories

HTH
JR

On Thu, 10 Oct 2019 at 05:24, Maryam Zolfaghar <
Maryam.Zolfaghar at colorado.edu> wrote:

>         External Email - Use Caution
>
> Hi all,
>
> I am trying to
>
>    - use decoders to decode whether ERP or time-frequency signals have
>    any meaninful information of four classes (location of the target on the
>    screen) in my experiment *over time *(according to this example
>    <https://mne.tools/stable/auto_tutorials/machine-learning/plot_sensors_decoding.html#decoding-over-time>
>    ).
>    -  and then test whether the output of the decoder is significantly
>    above the chance (in my case: 1/4=0.25) using a permutation t-test with
>    cluster-based correction.
>
> My question is:
>
>    - In the example
>    <https://mne.tools/stable/auto_tutorials/machine-learning/plot_sensors_decoding.html#decoding-over-time>
>    there are only two classes, so AUC was used. However, what if there are
>    more than two classes? How I can analyze the significance of the decoder's
>    output with the cluster-based correction?
>
>
> Thanks,
> -Mary
>
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