<p dir="ltr">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. </p>
<p dir="ltr">Eric<br>
</p>
<div class="gmail_quote">On Nov 11, 2015 6:01 AM, "Moorselaar, D. van" <<a href="mailto:d.van.moorselaar@vu.nl">d.van.moorselaar@vu.nl</a>> wrote:<br type="attribution"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div style="word-wrap:break-word">
<span style="font-family:HelveticaNeue">Hi all,</span>
<div style="font-family:HelveticaNeue"><br>
</div>
<div style="font-family:HelveticaNeue">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 (<a href="http://github.com/dvanmoorselaar/eeg_analysis" target="_blank">github.com/dvanmoorselaar/eeg_analysis</a>;
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. </div>
<div style="font-family:HelveticaNeue">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:</div>
<div style="font-family:HelveticaNeue"><br>
</div>
<div><font face="HelveticaNeue">session.filter(l_freq = 0.5, h_freq = None, filter_length = 3073, l_trans_bandwidth = 0.15)</font></div>
<div style="font-family:HelveticaNeue"><br>
</div>
<div style="font-family:HelveticaNeue">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).</div>
<div style="font-family:HelveticaNeue"><br>
</div>
<div style="font-family:HelveticaNeue"><img height="685" width="974" src="cid:05AE8DCA-D1A5-4AB4-BA6D-72A3B97E939D"></div>
<div style="font-family:HelveticaNeue">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.</div>
<div style="font-family:HelveticaNeue"><br>
</div>
<div style="font-family:HelveticaNeue">Is this a known problem or am I doing completely incorrect? </div>
<div style="font-family:HelveticaNeue"><br>
</div>
<div style="font-family:HelveticaNeue">Best,</div>
<div style="font-family:HelveticaNeue"><br>
</div>
<div style="font-family:HelveticaNeue">Dirk van Moorselaar</div>
</div>
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