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

Wed Aug 12 12:45:30 PDT 2015

Hi all,

Many thanks for everyone's very detailed and thoughtful comments, they've
been invaluably helpful!

I systematically compared 0.1hz vs. 1hz data using two ERP searchlights:
the more liberal cluster mass permutation test, and the much more
conservative tMax permutation test. I used FWER = 0.1, 5000 permutations,
and cluster size of ~3.15cm.

0.1hz:
https://cmu.box.com/s/99lp08a2aodkyufp9iwkg2lxldhegu1z

1hz:
https://cmu.box.com/s/th5w62gunm9c9q23j5806ra6zue2u686

In my interpretation, it seems like the 'drift' in the 0.1hz data really
does seem to be evoked infraslow components. 1hz filter reduces differences
between conditions (amplitude & t-scores), makes significance less
temporally cohesive, and eliminates late SW component. This is true for
both cluster permutation & tMax permutation tests.

I'm doing an object valence study and infraslow components are well
established in the literature (hopefully that's not inducing confirmation
bias :-P).

Detrending per epoch seemed to reduce significance but still need to do it
more systematically.

Thanks again,
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>

On Mon, Aug 10, 2015 at 2:52 PM, Mohammed Jarjees <
m.jarjees.1 at research.gla.ac.uk> wrote:

> Dear Kevin Tan,
> Sorry for this late reply. I was away last week.
>
> Yes, I mean use detrend on individual epochs. I hope this will help you to
> make your data better and after detrend you can apply the ICA wights from
> 1Hz data on 0.1 detrended data.
> Best Regards
>
> Mohammed Jarjees
> ________________________________________
> From: Kevin Tan [kevintan at cmu.edu]
> Sent: 06 August 2015 07:20
> To: makoto at sccn.ucsd.edu; Mohammed Jarjees; EEGLAB List
> Subject: Re: [Eeglablist] Too much drift with just 0.1hz high-pass
> filtering?
>
> Makoto,
>
> Thanks so much for the detailed reply. Indeed, given all the caveats,
> 0.1hz hi-pass produces 'better' ERP waveforms and is in-line with the
> literature.
>
> But what I'd really like to know is, does *my* 0.1hz data contain
> comparable drift to others' 0.1hz data? It's hard to tell since people
> don't publish their epoched EEG data. If it is comparable then I can feel
> much better.
>
> And yes, for ICA I use 1hz hipass then copy the resulting weights to 0.1hz
> data.
>
> Mohammed,
>
> I've tried detrend on continuous 0.1hz data with little effect. Do you
> mean detrend on individual epochs? Would that result in consistency issues
> across epochs?
>
> Many thanks for the comments!
>
> Best,
> 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<mailto:kevintan at cmu.edu> | tarrlab.org/kevintan<
> http://tarrlabwiki.cnbc.cmu.edu/index.php/KevinTan>
>
> On Thu, Aug 6, 2015 at 7:50 AM, Mohammed Jarjees <
> m.jarjees.1 at research.gla.ac.uk<mailto:m.jarjees.1 at research.gla.ac.uk>>
> wrote:
> Dear Kevin Tan,
> Have you tried detrend function on 0.1 Hz filtered data. It may be help.
> Best Regards
> Mohammed Jarjees
>
> ________________________________________
> From: Makoto Miyakoshi [mmiyakoshi at ucsd.edu<mailto:mmiyakoshi at ucsd.edu>]
> Sent: 05 August 2015 06:03
> To: Kevin Tan
> Cc: EEGLAB List
> Subject: Re: [Eeglablist] Too much drift with just 0.1hz high-pass
> filtering?
>
> 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.
>
> Makoto
>
> On Tue, Aug 4, 2015 at 10:20 PM, Kevin Tan <kevintan at cmu.edu<mailto:
> kevintan at cmu.edu><mailto:kevintan at cmu.edu<mailto: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
> - DIPFIT
> - 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<mailto:kevintan at cmu.edu><mailto:kevintan at cmu.edu
> <mailto:kevintan at cmu.edu>> | tarrlab.org/kevintan<
> http://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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