[Eeglablist] Too much drift with just 0.1hz high-pass filtering?

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Wed Aug 5 11:03:29 PDT 2015

Dear Kevin,

In the Appendix of Rousslet (2012), you can find the shockingly bad effect
of 1-Hz high-pass filter compared with 0.5-Hz or below. However, the filter
function used here is pop_eegfilt, which is an old generation before
Andreas Widmann redesign it, so for 500-Hz sampled data the filter order
for 1-Hz is 75000. After Andreas's fix, it is 1651. 75000 is crazy. You
need to keep it in mind. FYI, 1-Hz highpass with Hamming window and fiter
order 75000 results in the transition bandwidth of 0.022; Andreas's
heuristics suggests 1.

> Is the drift in 0.1hz data ok? I get 'better looking' ERP waveforms &
more robust differences between conditions in 0.1hz data – I'm worried this
is mostly due to drift.

Sadly, it is often the case that our eye are trained for something that
does not have a good ground. Rousslet (2012) showed 'distortion' of ERP
waveforms after 1- and 2-Hz highpass (but again with old function).
However, if you know Gibb's phenomenon etc and the exact filter order you
are using, you would find nothing wrong. Same goes for your/my impression
of the 0.1-Hz high-passed data. I would say the waves are drifting and at
least bad for the purpose of ICA. But for the researchers of EEG infraslow
oscillations, they would say oh it's a good data.

So there is no good or bad. After averaging several hundred trials, the
apparently drifting signals (to my eyes) will produce 'better' ERP
waveforms, thanks to the averaging process. If you say you will run ICA on
the 0.1-Hz highpassed data, I'd say you shouldn't.

Stephan Debener's solution is that you apply 1-Hz high-pass on the data,
run ICA, copy the weight matrix to the 0.1-Hz high-passed data.


On Tue, Aug 4, 2015 at 10:20 PM, Kevin Tan <kevintan at cmu.edu> wrote:

> Hi all,
> There are numerous papers that conclude that >0.1hz high-pass filtering
> distorts ERPs. However, I notice a lot of remaining drift after 0.1hz
> hi-pass, especially compared to 1hz hi-pass. I'm using a BioSemi Active2
> 128ch.
> 0.1hz hi-pass:
> https://cmu.box.com/s/1uafw786miveruz85ycj3taxflzg7p7f
> 1hz hi-pass:
> https://cmu.box.com/s/t1dbzntjcwdrsp734m949xnzmycvpw5p
> Is the drift in 0.1hz data ok? I get 'better looking' ERP waveforms &
> more robust differences between conditions in 0.1hz data – I'm worried this
> is mostly due to drift.
> The 1hz data has ERP 'distortions': negative slope from start of epoch
> until P1 & negative deflection of later components. Thus, I'm not
> comfortable with either of the filters.
> The screenshots show data run only through 1) PREP pipeline 2) high-pass
> filtering 3) epoching. The final cleaned data shows the same drift.
> My full preproc stream:
> ICA dataset:
>> - Load PREP'd data
>> - 1hz hi-pass
>> - Epoch
>> - Epoch rejection
>> - Extended ICA (binica)
>> - Determine bad ICs
> Final dataset:
>> - Load (unfiltered) PREP'd data
>> - 0.1hz hi-pass (tried 1hz for comparison too)
>> - Epoch
>> - Generate ICs from matrices of ICA dataset
>> - Remove bad ICs determined from ICA dataset
>> - Epoch rejection
>> - Make ERPs
> Any input would be much appreciated!
> Many thanks,
> Kevin
> --
> Kevin Alastair M. Tan
> Lab Manager/Research Assistant
> Department of Psychology & Center for the Neural Basis of Cognition
> Carnegie Mellon University
> Baker Hall 434
> <https://www.google.com/maps/place/40%C2%B026%2729.5%22N+79%C2%B056%2744.0%22W/@40.4414869,-79.9455701,61m/data=!3m1!1e3!4m2!3m1!1s0x0:0x0>
>  | kevintan at cmu.edu | tarrlab.org/kevintan
> <http://tarrlabwiki.cnbc.cmu.edu/index.php/KevinTan>
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Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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