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<http://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:
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).
[cid:05AE8DCA-D1A5-4AB4-BA6D-72A3B97E939D]
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?
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
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:
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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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.
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.
