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

Thu Aug 13 15:20:54 PDT 2015

Dear Kevin,

Thanks for sharing the results of nice comparison (and good demonstration
of David's tool).

> In my interpretation, it seems like the 'drift' in the 0.1hz data really
does seem to be evoked infraslow components.

You mean 0.1-1.0Hz.

> 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 agree. The EEG activities distribute smoothly across frequency ranges
anyways, so it is not surprising. I would also find <1Hz activities in all
of my past P300 studies etc.

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

Yes why not following the literature.

Makoto

On Wed, Aug 12, 2015 at 12:45 PM, Kevin Tan <kevintan at cmu.edu> wrote:

> 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
>>
>
>

-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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