[Mne_analysis] Amplitude of interpolated channels very small

[Christian Kiefer]

        External Email - Use Caution        

Hi,

I used visual inspection to mark the bad channels. Basically it came
down to:

# load raw data
import mne
raw_fname = '/path/to/file'
raw = mne.io.Raw(raw_fname, preload=True)
# mark bads
raw.plot(block=True)
raw.interpolate_bads(reset_bads=True)

Hope that helps!

Christian


On 08.05.18 15:09, Mainak Jas wrote:
>
>         External Email - Use Caution        
>
> Hi,
>
> Can you also share a script to demonstrate the problem?
>
> Thanks
>
> Mainak
>
> On Tue, May 8, 2018 at 9:21 AM, Christian Kiefer
> <ch.kiefer at fz-juelich.de <mailto:ch.kiefer at fz-juelich.de>> wrote:
>
>             External Email - Use Caution       
>
>     Hello everyone,
>
>     most of the time interpolate_bads() works well but I have some
>     data sets
>     where the amplitude of the interpolated channels is significantly
>     smaller than the amplitudes of the surrounding channels.
>
>     In the following Dropbox link are two examples of data sets before
>     interpolation and after interpolation (marked with 'bcc' in the
>     filename). Channels 7, 10, 142, and 156 are dead in the original data
>     sets. After interpolation, the channels still appear to be dead/ flat
>     when plotting the data, however, looking at the raw data for these
>     channels we can see that the values are now different from zero.
>     Redoing
>     the interpolation yields the same result.
>
>     https://www.dropbox.com/sh/11rjc45bbg2b38m/AACUW6rtZKcJKitEMfbMIKVqa?dl=0
>     <https://www.dropbox.com/sh/11rjc45bbg2b38m/AACUW6rtZKcJKitEMfbMIKVqa?dl=0>
>
>     Why does the interpolation work for some data sets and not for others?
>
>     Best,
>
>     Christian Kiefer
>
>
>
>
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