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

Thu Aug 6 15:58:15 PDT 2015

Dear Kevin,

> But what I'd really like to know is, does *my* 0.1hz data contain
comparable drift to others' 0.1hz data?

Yes, your data look normal for 0.1Hz high-passed.
I also recommend you change the time scale so that you can see 10 sec long
of data in one screen to visually confirm if there is any wave whose half
cycle (i.e. single peak or trough) goes beyond  5 sec.

Widmann et al. (2014), who advocates the merit of filter, wrote:

%%%%%%%
Some authors argue against high-pass filtering (or restrict the applicable
high-pass cutoff to frequencies as low as <0.1 Hz; in particular if
estimating window mean or peak amplitudes; Acunzo et al.,2012; Luck, 2005)
or low-pass filtering (in
particular if estimating onset latencies; VanRullen, 2011). We certainly
want to stress their point–care is needed–but, on the other hand, if the
filter applied really increases the signal-to-noise ratio (as it should to
motivate its usage) and does not systematically bias the to-be-estimated
parameter, these values can be determined with greater precision with than
without filtering.
%%%%%%%

Exaggerated filter phobia is not useful.
A common mistake is to think that
'Closer to the original, better the signal'
'Therefore, less use of filter, better the signal'
Here, the underlying idea is 'minimizing the use of filter == maximizing
signal *fidelity*' But it does not apply to our case; we are not playing
music where the original source necessarily has the maximum signal
fidelity. Andreas Widmann's point is 'Hey, what matters is signal-to-noise
ratio; if filtering can improve it, why not use it rather than avoiding
it?' This makes sense to me.

However, I'm not sure how to measure the signal to noise ratio.
In our case, we have been using ICA + mutual information reduction to
evaluate the goodness of preprocessing, but this is kind of ICA-centric
view and may not be acceptable for others.

Makoto

On Thu, Aug 6, 2015 at 12:20 PM, Kevin Tan <kevintan at cmu.edu> wrote:

> 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 | 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> 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]
>> 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>> 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> | 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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