Amplitude of interpolated channels very small

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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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Hi,

Can you also share a script to demonstrate the problem?

Thanks

Mainak

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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

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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