[Mne_analysis] jump artifacts after filtering

[Denis-Alexander Engemann]

General note,

if someone has the time and motivation to track down the subtle differences
in parametrisation of matlab VS scipy/mne filters I will pay that person a
huge beer next time we meet :)
There is definitely something to be understood and improved in that corner.

Cheers,
Denis


On Wed, Nov 11, 2015 at 3:27 PM, Moorselaar, D. van <d.van.moorselaar at vu.nl>
wrote:

> Thank you for the quick response. I was running version 0.9. I have
> updated now to version 0.10 and the jump artifacts are indeed no longer
> present.
> There appears to be a offset shift though between matlab and mne filter
> (Matlab being consistently more negative), but I will look into this.
> Thank you very much.
>
> Best,
>
> Dirk
>
>
> On Nov 11, 2015, at 12:17 PM, Eric Larson <larson.eric.d at gmail.com> wrote:
>
> Which version are you running? There was a bug some months ago that
> produced some jump artifacts, but it has been fixed in 0.10 (latest
> release) and master.
>
> Eric
> On Nov 11, 2015 6:01 AM, "Moorselaar, D. van" <d.van.moorselaar at vu.nl>
> wrote:
>
>> Hi all,
>>
>> At the moment I am working on a project in which I am comparing a
>> semi-automatic preprocessing procedure in Matlab (using EEGlab and
>> Fieldtrip toolboxes) to preprocessing in python using MNE (
>> github.com/dvanmoorselaar/eeg_analysis; still in development). I am able
>> to exactly replicate the results from the Matlab pipeline, potentially
>> allowing us to move all our eeg analyses to mne/python.
>> There is one problem however. While comparing the output in Matlab to
>> that in python per preprocessing step I noticed some weird jump artifacts
>> after applying a 0.5 high pass filter to the data:
>>
>> session.filter(l_freq = 0.5, h_freq = None, filter_length = 3073,
>> l_trans_bandwidth = 0.15)
>>
>> As visualized below (Matlab in red, Python in green), after referencing
>> the data are perfectly aligned (top figures). However, after applying a 0.5
>> high pass filter with the settings specified above (default Matlab
>> settings), the data now all of a sudden contain jump artifacts. These
>> artifacts are present at random intervals throughout the whole time series
>> and in all channels. At first I thought this might have something to do
>> with the chosen filter length (MNE give a warning that filter length should
>> be increased), however when I increased the filter length three times these
>> artifacts were still present (bottom right) in the data, although less
>> frequent and at different time points).
>>
>> <filter_artifact.png>
>> Also, I noticed that similar jump artifacts were present when applying a
>> 0.5 low pass filter instead of a high pass filter, potentially indicating
>> that something is off in the numpy fft convolution.  At first sight there
>> appeared nothing wrong with the power spectra of these filters.
>>
>> Is this a known problem or am I doing completely incorrect?
>>
>> Best,
>>
>> Dirk van Moorselaar
>>
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