[Eeglablist] Inconsistent results using clean_artifacts

Thu Mar 17 10:57:19 PDT 2022

Dear Cristina,

Wow, this is such a perfect summary report. I deeply appreciate you took so
much time and care to make this happen.
You are the best part of the EEGLAB mailing list. Thank you, thank you,
thank you!

> Second, I would prefer not to discard the RANSAC method to detect bad
channels if I find a stable solution. I believe that the RANSAC method is
the core for detecting bad channels in the clean_rawdata function.

I appreciate you mentioning that. I'll tell you why.
In the early 2010's when Christian, the developer of ASR, was working on
the offline version of clean_rawdata() upon my request, he gave me a
solution once, then told me that he wanted to add one more thing for
update. Within a few days, this RANSAC part was implemented. So this RANSAC
part was one of the final touch ups he specifically wanted to implement.

So I agree, I'd love to use his bad-channel rejection. Your confirmation is
so valuable for me--increasing the 'NumSamples' to 1000, for example, can
make the algorithm's behavior more stable. I'll make it my default and use
the channel rejection function again. I still would not use 0.8 for the
correlation criterion though, I'd use 0.6-0.7. Christian did recommend
higher values. But the problem of channel rejection is that short-segment
of high-amplitude data always biases the selection. Now I quickly checked
code of clean_channels(), but the current process is not robustified
against the short, high-amplitude burst.
https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_sccn_clean-5Frawdata_blob_master_clean-5Fchannels.m&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=7MRRc_eGF-zRnOHV91JuP69nH_DfJ_n8__4plFJ1ZBKv3AVDiesfQ1_aw4kiinqe&s=n9mAJ77Yi_qwB6Y7iYBF_ArTtzLZVIVclGz_i73SBUQ&e= 
It seems possible to address this issue. I'll discuss it with colleagues.

By the way, I have an update for you ASR enthusiasts which you may be
interested in. Let me forward my recent post to the list below.

%%%%%%%%%%
Relatedly, Hyeonseok and I have been working on a mod for the calibration
stage of ASR to process our Juggling data collected by Hiroyuki.
We will present the idea at the Mobi meeting 2022.
https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_ucsd.edu_mobi2022_&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=7MRRc_eGF-zRnOHV91JuP69nH_DfJ_n8__4plFJ1ZBKv3AVDiesfQ1_aw4kiinqe&s=KDRzeEmSY0fvxg-xmUJz9cAcmzT2cOEem7fPJzGTKwg&e= 

The idea is to use single-frame order statistics across electrodes rather
than the default sliding window for selecting the calibration data. This
way, we can obtain more 'clean' data points without letting high-amplitude
artifacts into the calibration data (there is a default tolerance
value--that is, the default setting allows a small amount of outliers sneak
into the calibration data, up to 7.5% of electrodes; The proposed
method uses 0%.) The proposed method makes subsequent PC distributions more
Gaussian, which fits the assumption of ASR. Also, the proposed method seems
to be able to explain, at least partially, the reason why the
conventional empirically recommended values for the cutoff SD are unusually
high, such as SD == 20. We will show both simulation and empirical results.
Check out the MoBI 2022 conference!
%%%%%%%%%%

Makoto

On Mon, Mar 14, 2022 at 8:55 AM Gil Avila, Cristina <cristina.gil at tum.de>
wrote:

> Thank you all for your input.
>
>
>
> First, I have noticed that the set of bad channels is only different every
> time I restart EEGLab (please see the code below, I run EEGLab command
> inside the loop over repetitions). Otherwise results are stable (@Arno
> Could this explain why it passed all the tests?).
>
>
>
> Second, I would prefer not to discard the RANSAC method to detect bad
> channels if I find a stable solution. I believe that the RANSAC method is
> the core for detecting bad channels in the clean_rawdata function. The two
> other options (clean channels based on flat line and on the high frequency
> activity) seem to me more a preliminary step to the RANSAC. Therefore I
> have tested:
>
>    1. How the ‘ChannelCriterion’ parameter influences the selected bad
>    channels. I have tried the values 0.7, 0.8 (default) and 0.9. The higher
>    the value, the less reproducible is the result. This was not a surprise if
>    I look at the definition of the ChannelCriterion parameter: ‘if a channel
>    is correlated at less than this value to an estimate based on other
>    channels it is considered abnormal in the given time window’. Still, even
>    being lax with the correlation threshold (0.7) I don’t get reproducible
>    results.
>    2. How the high-pass bandwidth influences the selected bad channels. I
>    have tried a highpass with bandwidth [1 1.5] instead of the default [0.25
>    0.75] with the ‘ChannelCriterion’ parameter fixed at 0.8. This does not
>    seem to increase the reproducibility.
>    3. How the ‘NumSamples’ RANSAC parameter of clean_artifacts()
>    influences the selected bad channels. I have tried with 50 (default), 100,
>    500 and 1000 samples with ‘ChannelCriterion’ fixed at 0.8. Increasing this
>    parameter to 1000 makes the output more reliable at the cost of more
>    computation time (~1.5 min per recording).
>
>
>
> Brief comment regarding my data: I am working with eyes-closed
> resting-state, 29 channels, recordings of 5 mins of duration sampled at 500
> Hz (~150000 samples).
>
> For each case I have run 10 repetitions. You can also find along with the
> code figures of all test cases. Figures represent how often was each
> channel marked bad in each recording.
>
>
>
> For reproducibility I attach my code and the small dataset I am using. I
> am using most recent versions of EEGLab and clean_rawdata from github.
>
> Code: https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_crisglav_replication-5Fclean-5Frawdata_&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=7MRRc_eGF-zRnOHV91JuP69nH_DfJ_n8__4plFJ1ZBKv3AVDiesfQ1_aw4kiinqe&s=tNKEH0_xyhr-yK22ZbpgtuY98E9kUvlO0nBsKTD53_M&e= 
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_crisglav_replication-5Fclean-5Frawdata_&d=DwQGaQ&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=9m75cEFE25pnZqvTCnezRor87-PYdjeB2KlL4FhRwDsyrde-Zy2fdp5Ds1Jye6IK&s=bRRPLy36GAqMvYFcRiHOH3FY3hXoxi1qCMMcxJ7EVPA&e=>
>
> Dataset:
> https://urldefense.proofpoint.com/v2/url?u=https-3A__syncandshare.lrz.de_getlink_fiX7VwVdbGEsMTf46kqrcvx3_rawBIDS&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=7MRRc_eGF-zRnOHV91JuP69nH_DfJ_n8__4plFJ1ZBKv3AVDiesfQ1_aw4kiinqe&s=PoBFoIC5mclsh6NIgl4FeQMUcrS9_Rzdq1UVlFS2H18&e= 
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__syncandshare.lrz.de_getlink_fiX7VwVdbGEsMTf46kqrcvx3_rawBIDS&d=DwQGaQ&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=9m75cEFE25pnZqvTCnezRor87-PYdjeB2KlL4FhRwDsyrde-Zy2fdp5Ds1Jye6IK&s=QCLa_vSG7bTiSAxeN1p8HNpZxCvMcpRp02JstwyDFMA&e=>
>
> Note: to test 3) I had to change clean_artifacts code and add in line 186
>
> {'num_samples','NumSamples'}, 50, ... % line 186
>
> And substitute line 232 by
>
> [EEG,removed_channels] =
> clean_channels(EEG,chancorr_crit,line_crit,[],channel_crit_maxbad_time,num_samples);
>
>
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> --
>
> Cristina Gil Ávila – PhD candidate
>
> Department of Neurology
>
> Technische Universität München
>
> Munich, Germany
>
> cristina.gil at tum.de
>
> painlabmunich.de
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.painlabmunich.de_&d=DwMGaQ&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=9m75cEFE25pnZqvTCnezRor87-PYdjeB2KlL4FhRwDsyrde-Zy2fdp5Ds1Jye6IK&s=hSQglHuzdgnx2GiKB_bxC1oRqVi-TqsmKbANR39Pcdk&e=>
>
>
>