[Mne_analysis] jump artifacts after filtering

[Eric Larson]

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).
>
> 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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