[Mne_analysis] Amplitude of interpolated channels very small

[Alexandre Gramfort]

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hi Christian,

can you open an issue here:

https://github.com/mne-tools/mne-python/issues

so we can follow up on this?

provide as much details as possible (code snippet, figure, data)

thanks
Alex


On Tue, May 8, 2018 at 4:29 PM, Christian Kiefer
<ch.kiefer at fz-juelich.de> wrote:
>         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>
> 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
>>
>> Why does the interpolation work for some data sets and not for others?
>>
>> Best,
>>
>> Christian Kiefer
>>
>>
>>
>>
>>
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