[Eeglablist] How to Clean EEG Data with Large Amplitudes After High-Pass Filtering
Cedric Cannard
ccannard at protonmail.com
Thu Jun 25 10:39:31 PDT 2026
Hi Jiongjiong,
These data look deeply corrupted. Or is what you are showing the period before the experiment starts, when participants are still moving a lot?
Some electreodes are heavily contaminated by power line noise, suggesting they are not shielded properly, or there is something wrong with the common-mode noise reduction (CMMR). And what is your reference here? and what are you trying to do? Allowing participants to move freely is typically NOT recommended for EEG unless you are studying motion directly like MobiLab at UCSD. If so, you would have more modalities to control for these movements, etc. (EMG, cameras, IMU, etc.). See UCSD's websites for more info on this: https://urldefense.com/v3/__https://github.com/sccn/mobilab/wiki/MoBILAB__;!!Mih3wA!CZCQ7e0D2v42I2wFLoub1Y_2I066-xmpJUPOwNwj9adb9PT-hZH5X1LrDwaImG7N8dm2PPlG2gPnMvYncky4g0GJDA$
Unfortunately, I would qualify these time series visible on your plot as unusable, even if you use the best algorithms. As they say, "trash in, trash out".
Since you recorded 80 files and will probably move forward, a few comments.
- Remove very bad portions before and after the experiment starts, that will help the algorithms
- your signal still contains power line artifacts after zipline in some electrodes. I personally prefer just applying a lowpass filter at ~45 or 30 Hz, depending on the research goals of course.
- you could normally detect and remove bad channels with the clean_channels EEGLAB function, via the cross-channel correlation method. But here, I doubt it will work since there is so much variance across your channels, even in the "no electrode coordinates" mode that applies a very conservative threshold. You could give it a try assuming you can tell visually if it's doing the job correctly or not. I would normally say you should apply some re-referencing, but usual methods (CAR, REST) are mathematically invalid with so few channels.
- you would normally inteprolate the bad channels you removed, but with this montage, I am not sure it would be reliable, especially on the edges like FP1
- ICLabel is not reliable with this type of montage, it was trained on 64-channels datar referenced to average. You can't use it here.
- I have worked with noisy wearable, low-density data, try (after these previous steps above) ASR with very lax threshold (e.g. 100) to remove (not reconstruct) very bad portions (assuming your design supports it and can remove portions) -> run ICA (infomax) -> check the components topographies and time series -> Your top component is likely motion and 2nd ocular -> remove them (but you neeed to have some experience to confirm that is the right choice) -> run ASR again more conservative (e.g. 20-40) in reconstruct mode -> use robust statistics for your analyses (if epoched data, use WLS GLMs as implemented in LIMO-EEG to downweight remaining bad epochs. Use robust nonparametric permutation statistics with TFCE correction for FWER.
All these choices depend on your study, data, design, goals etc. of course, but that's a general strategy I would start with. If you are not familiar with all these concepts, I strongly recommend you educate yourself first with the UCSD wiki and youtube videos (e.g. starting here: https://urldefense.com/v3/__https://eeglab.org/tutorials/06_RejectArtifacts/Channel_rejection.html__;!!Mih3wA!CZCQ7e0D2v42I2wFLoub1Y_2I066-xmpJUPOwNwj9adb9PT-hZH5X1LrDwaImG7N8dm2PPlG2gPnMvYnckztjrjtzQ$ ).
Good luck
Cedric
Sent with Proton Mail secure email.
On Thursday, June 25th, 2026 at 7:06 AM, Jiongjiong Li via eeglablist <eeglablist at sccn.ucsd.edu> wrote:
> Hello Tomas,
> Many thanks for your kind and helpful guidance!
>
> I preprocessed the EEG data following:
> 1) Apply 1 Hz high-pass filter
> 2) Apply Zapline for line-noise removal
> 3) Run GEDAI with auto+ and *bad-channel rejection enabled*
>
> GEDAI identified FP1 as a bad channel. Therefore, I used the following
> pipeline:
> 1) *Remove FP1* from the raw data
> 2) Apply 1 Hz high-pass filter
> 3) Apply Zapline for line-noise removal
> 4) Run GEDAI with auto+ and *bad-channel rejection disabled* (because it
> remove 3 channels otherwise).
> Overall, GEDAI appears to have substantially improved the data quality:
>
> 1. Only 0.05% of samples have |EEG| > 100 µV.
> 2. Amplitude distribution appears approximately Gaussian.
> 3. Channel data is much cleaner.
> 4. Channel spectra show substantially reduced power.
> 5. ICA classification identifies more ICs as brain-related.
>
> However, I still mainly have two concerns:
> 1*. SSI Silhouette Score is only 0.36*, which seems relatively low.
> 2.GEDAI-removed data contained 12/15 ICs labeled as Brain. This may
> indicate GEDAI removed some neural activity, or that ICA classification is
> not reliable in this context.
>
> Details:
>
> 1. Percentage of samples with |EEG| > 100 µV per channel after each
> processing stage:
>
> ----------------------------------------------------------------
> Channel Highpass (1Hz) Zapline GEDAI
> ----------------------------------------------------------------
> FP2 31.31% 27.22% 0.03%
> C3 37.70% 35.13% 0.09%
> C4 34.83% 31.82% 0.07%
> P7 77.78% 77.61% 0.03%
> P8 50.24% 49.89% 0.03%
> O1 85.52% 85.28% 0.04%
> O2 52.29% 51.35% 0.21%
> F7 27.34% 24.77% 0.01%
> F8 42.44% 40.04% 0.02%
> F3 43.28% 42.94% 0.02%
> F4 34.78% 34.07% 0.04%
> T7 25.87% 23.31% 0.03%
> T8 27.63% 24.45% 0.03%
> P3 87.47% 86.89% 0.01%
> P4 69.98% 69.90% 0.04%
> Total 48.56% 46.98% 0.05%
>
> 2. Additional comparison figures after each stage:
>
> 2.1 Amplitude distribution:
> https://urldefense.com/v3/__https://drive.google.com/file/d/1iGUhps4UtATY1c060NV4rSllSrZQ5ION/view?usp=sharing__;!!Mih3wA!CaIG6ShccYM_SNsw1TOgp23Oeh4OtDBhcfGnFD9cxrOkA6TYzg9HWnfz3TUyv-XotIh8RbLYojsWzQGua5B82QZo5XmM$
>
> 2.2 Channel data:
> https://urldefense.com/v3/__https://drive.google.com/file/d/1wjqGVyjTJXtzu9cMzbdtJi0azVmUcK2H/view?usp=sharing__;!!Mih3wA!CaIG6ShccYM_SNsw1TOgp23Oeh4OtDBhcfGnFD9cxrOkA6TYzg9HWnfz3TUyv-XotIh8RbLYojsWzQGua5B82QMlq3-p$
>
> 2.3 Spectra:
> https://urldefense.com/v3/__https://drive.google.com/file/d/1mxaHMrenmd3LiZoM6jnlSo5rEhpPpWaP/view?usp=drive_link__;!!Mih3wA!CaIG6ShccYM_SNsw1TOgp23Oeh4OtDBhcfGnFD9cxrOkA6TYzg9HWnfz3TUyv-XotIh8RbLYojsWzQGua5B82Qq9Qv0I$
>
> 2.4 ICA results before vs after GEDAI:
> https://urldefense.com/v3/__https://drive.google.com/file/d/1sX8davJ3TK6ShjIy1GfgUHIwALuZ1PtT/view?usp=drive_link__;!!Mih3wA!CaIG6ShccYM_SNsw1TOgp23Oeh4OtDBhcfGnFD9cxrOkA6TYzg9HWnfz3TUyv-XotIh8RbLYojsWzQGua5B82Zp8RzBk$
> • Before GEDAI: 11/15 ICs labeled as brain, 2 as other, 2 as line noise
> • After GEDAI: 14/15 ICs labeled as brain, 1 as other
>
> 2.5 Data Removed by GEDAI (Identified as Artifacts)
> 2.5.1 Spectra of data before GEDAI, data removed by GEDAI (Identified as
> artifacts), and remaining data (identified as clean):
> https://urldefense.com/v3/__https://drive.google.com/file/d/1hK4H_hTEFm5D1Otm6G9z6k61GaSoftX3/view?usp=sharing__;!!Mih3wA!CaIG6ShccYM_SNsw1TOgp23Oeh4OtDBhcfGnFD9cxrOkA6TYzg9HWnfz3TUyv-XotIh8RbLYojsWzQGua5B82TrSE9ZD$
>
> 2.5.2 ICA results of data before GEDAI vs data removed by GEDAI
> (Identified as Artifacts):
> https://urldefense.com/v3/__https://drive.google.com/file/d/10S03EQmlTjTMM7NuCbPdEh2b-LvsAx0o/view?usp=sharing__;!!Mih3wA!CaIG6ShccYM_SNsw1TOgp23Oeh4OtDBhcfGnFD9cxrOkA6TYzg9HWnfz3TUyv-XotIh8RbLYojsWzQGua5B82e3h3kYi$
> • Before GEDAI: 11/15 ICs labeled as brain, 2 as other, 2 as line noise
> • Data removed by GEDAI: 12/15 ICs labeled as brain, 2 as other, 1 as line
> noise
>
> 3. SENSAI visualization:
> https://urldefense.com/v3/__https://drive.google.com/file/d/1ngKiMtEp0U3BvUnkiiL2Qtzz1rYYcUSo/view?usp=drive_link__;!!Mih3wA!CaIG6ShccYM_SNsw1TOgp23Oeh4OtDBhcfGnFD9cxrOkA6TYzg9HWnfz3TUyv-XotIh8RbLYojsWzQGua5B82Yi55Dqa$
> • SSI Silhouette Score: 0.36
> • SENSAI score: 39%
> • ENOVA: 98%
> • Mean SSSI Before Denoising: 0.51
> • Mean SSSI After Denoising: 0.78
> • Mean NSSI After Denoising: 0.50
>
> Any comments or suggestions would be very welcome!
>
> Sincerely,
> Jiongjiong
>
>
> On Wed, Jun 24, 2026 at 11:14 AM Ros, Tomas <dr.t.ros at gmail.com> wrote:
>
> > Hello Jiongjiong,
> >
> > Having looked at the figures you sent, allow me to chip in regarding GEDAI
> > denoising.
> >
> > Overall, the data you have is very noisy with quite a low channel count
> > (16 channels). Your initial parameters are pretty much appropriate for this
> > type of data (auto+ with a 1 Hz low cut). To further improve the final
> > result I would recommend:
> >
> > 1. Remove the line noise BEFORE GEDAI (using a notch or low pass filter
> > <55 Hz, or even better using Zapline)
> > 2. Enable bad channel rejection within GEDAI (with default ENOVA of 0.95).
> >
> > That should further remove the very noisy parts in the data. For a deeper
> > dive, check out the GEDAI FAQ
> > <https://urldefense.com/v3/__https://github.com/neurotuning/GEDAI-master/wiki/Frequently-Asked-Questions-(FAQ)__;!!Mih3wA!CaIG6ShccYM_SNsw1TOgp23Oeh4OtDBhcfGnFD9cxrOkA6TYzg9HWnfz3TUyv-XotIh8RbLYojsWzQGua5B82QD_2Mk8$ >
> >
> > Good luck and all the best,
> > Tomas
> >
> > ▬▬▬
> >
> > *Tomas Ros, PhD*
> >
> > Lecturer, Department of Clinical Neurosciences
> > CIBM EEG HUG-UNIGE Section
> > University of Geneva, Switzerland
> > https://urldefense.com/v3/__https://www.tomasros.com/__;!!Mih3wA!CaIG6ShccYM_SNsw1TOgp23Oeh4OtDBhcfGnFD9cxrOkA6TYzg9HWnfz3TUyv-XotIh8RbLYojsWzQGua5B82ZCkXpaA$
> >
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> >
> > On Wed, 24 Jun 2026 at 17:48, Jiongjiong Li via eeglablist <
> > eeglablist at sccn.ucsd.edu> wrote:
> >
> >> Hello Евгений, Ingmar and Eugen,
> >>
> >> I deeply appreciate all of your support and invaluable guidance!
> >>
> >> Since our lab has already collected 80 recordings under the same
> >> condition,
> >> my initial goal is to clean the existing dataset. Following Ingmar's
> >> suggestion, I applied *GEDAI *(
> >> https://urldefense.com/v3/__https://github.com/neurotuning/GEDAI-master__;!!Mih3wA!DP_6iMSxhZ5nGMQgcR6NRZFO5LvXiIRNlofhI1K7OEXLKjqDu5TDmv_VUkJro-DS5xoeAc3e1MK9JWLcSr9RAoNOuMWN$
> >> ) to
> >> preprocess the raw EEG data. After cleaning, the proportion of samples
> >> with *absolute
> >> EEG amplitude > 100 µV was reduced to 8.89%*, making further analysis
> >> feasible.
> >>
> >> Below are the data-denoising results using GEDAI:
> >> 1. EEG signal 1 Hz high-pass only vs. GEDAI:
> >>
> >> https://urldefense.com/v3/__https://drive.google.com/file/d/17dnk7SIsP3gMiFzAuPG4WTmkBTIF8QLw/view?usp=sharing__;!!Mih3wA!DP_6iMSxhZ5nGMQgcR6NRZFO5LvXiIRNlofhI1K7OEXLKjqDu5TDmv_VUkJro-DS5xoeAc3e1MK9JWLcSr9RAhhcH-Pk$
> >>
> >> 2. EEG spectra Hz high-pass only vs. GEDAI:
> >>
> >> https://urldefense.com/v3/__https://drive.google.com/file/d/1vnjrqhCCGH_K_oaU_eSwMGKwCh0ygWHO/view?usp=sharing__;!!Mih3wA!DP_6iMSxhZ5nGMQgcR6NRZFO5LvXiIRNlofhI1K7OEXLKjqDu5TDmv_VUkJro-DS5xoeAc3e1MK9JWLcSr9RAiTBeN8A$
> >>
> >> 3. ICA results after GEDAI (all ICs labeled as “brain”):
> >>
> >> https://urldefense.com/v3/__https://drive.google.com/file/d/10NaJlz2FK7TVuvCT1F6hf7vpq00zciVs/view?usp=drive_link__;!!Mih3wA!DP_6iMSxhZ5nGMQgcR6NRZFO5LvXiIRNlofhI1K7OEXLKjqDu5TDmv_VUkJro-DS5xoeAc3e1MK9JWLcSr9RAmoNmDhg$
> >>
> >> Note: GEDAI was performed using the auto-denoising option with auto+
> >> strength and a 1 Hz high-pass filter.
> >>
> >> For comparison, EEG spectra before and after ASR denoising are shown
> >> below:
> >>
> >> https://urldefense.com/v3/__https://drive.google.com/file/d/1AEOCy-MxqGBQzaU-_1lgVlmNR7_evLFd/view?usp=sharing__;!!Mih3wA!DP_6iMSxhZ5nGMQgcR6NRZFO5LvXiIRNlofhI1K7OEXLKjqDu5TDmv_VUkJro-DS5xoeAc3e1MK9JWLcSr9RAnyBFCsN$
> >>
> >> The table below summarizes the percentage of samples with absolute EEG
> >> amplitude > 100 µV after each denoising method:
> >>
> >> Channel Percent of abs(EEG) > 100uV
> >> High Pass (1 HZ) ASR GEDAI
> >> FP1 92.946 Removed 2.5073
> >> FP2 31.312 Removed 3.0691
> >> C3 37.702 0.97549 8.0592
> >> C4 34.826 1.6984 7.4946
> >> P7 77.775 53.236 17.479
> >> P8 50.242 10.092 10.864
> >> O1 85.518 73.37 22.544
> >> O2 52.286 Removed 8.4524
> >> F7 27.337 1.5796 3.816
> >> F8 42.444 17.465 6.726
> >> F3 43.282 3.4959 11.055
> >> F4 34.779 0.29215 7.5504
> >> T7 25.874 0.52488 2.4978
> >> T8 27.634 Removed 2.6692
> >> P3 87.468 71.226 16.299
> >> P4 69.978 30.943 11.147
> >> Total 51.34% 22.07% 8.89%
> >> Note: ASR removed 70% samples.
> >>
> >> I would also appreciate your guidance on the following questions:
> >>
> >> *1. Existing EEG data cleaning*
> >> 1.1 Do the data look acceptable after GEDAI cleaning? If not, are there
> >> additional remediation approaches for the existing dataset?
> >> 1.2 Is it acceptable to apply further preprocessing steps (e.g., notch
> >> filtering) after GEDAI, given the presence of a 60 Hz peak in the
> >> spectrum?
> >>
> >> *2. Improving future EEG recordings*
> >> 2.1 Ingmar mentioned that “100 µV is not unusual with moving
> >> participants.”
> >> May I further confirm whether 100 µV is still expected in mobile EEG under
> >> our experimental conditions? Specifically, each trial lasts more than 20
> >> minutes, and participants are allowed to walk and speak to simulate a
> >> natural environment.
> >> 2.2 Could you recommend any books, videos, or resources for learning how
> >> to
> >> identify EEG artifacts? I would greatly like to strengthen my
> >> understanding
> >> in this area.
> >>
> >> Thank you very much again for your time and guidance!
> >>
> >> Sincerely,
> >>
> >> Jiongjiong Li
> >> Department of Computer Science
> >> Illinois Institute of Technology
> >> 10 W 35th St, Chicago, IL 60616
> >>
> >>
> >>
> >> On Tue, Jun 23, 2026 at 2:19 AM Евгений Машеров <emasherov at yandex.ru>
> >> wrote:
> >>
> >> > Frequency filtering can be helpful if the artifact is concentrated in a
> >> > fairly narrow frequency band outside the region of primary interest.
> >> > However, if the artifact's shape is anything other than sinusoidal, it
> >> > cannot be completely eliminated, since in addition to the fundamental
> >> > frequency, there are also harmonics, which typically fall within the
> >> band
> >> > of primary research interest. Frequency filtering is helpful, for
> >> example,
> >> > in removing artifacts associated with electrode polarization or galvanic
> >> > skin response, at the cost of losing some of the delta range. It is also
> >> > possible to attempt to eliminate muscle artifacts by losing some of the
> >> > beta range. This type of filtering aids visual analysis, but is rather
> >> > detrimental to mathematical processing.
> >> > Independent component analysis has already been proposed here, and it
> >> can
> >> > be a lifesaver if the component caused by motion is correctly
> >> identified.
> >> > However, if there is no signal at all, nothing will help. And in the
> >> > figure shown, at least one channel (FP1) appears extremely unreliable.
> >> The
> >> > presence of power line interference in one channel often indicates a
> >> > complete lack of contact with the scalp. Power line interference occurs
> >> due
> >> > to capacitive coupling between the electrical network and the electrode,
> >> > and since the area of the "capacitor plates" (EEG electrode and power
> >> > cable) is small and the distance between them is quite large, the
> >> > capacitance is on the order of tenths of a picofarad or less, while the
> >> > resistance at 50 or 60 Hz is tens or hundreds of gigaohms. If the
> >> electrode
> >> > and scalp contact are sufficiently good (resistance 10 kiloohms or
> >> less),
> >> > the resulting voltage divider attenuates the power line interference by
> >> > several million times, and it is not detected. If contact with the
> >> scalp is
> >> > broken, the attenuation is equal to the ratio of the amplifier's input
> >> > impedance to the resistance of the capacitive coupling to the electrical
> >> > cables. If interference appears on one of the electrodes, it should be
> >> > adjusted.
> >> > Motion artifacts may be due to a cap that is too loose. Only after
> >> > correcting all the causes of artifacts appearing during recording does
> >> it
> >> > make sense to use mathematical methods for their elimination, primarily
> >> ICA.
> >> >
> >> > Your truly
> >> >
> >> > Eugen Masherov
> >> >
> >> > > Hello,
> >> > > I'm working with EEG data collected using a Cyton + Daisy setup. The
> >> > > experiment allows participants to move freely throughout the
> >> recording,
> >> > so
> >> > > I expected a significant amount of motion-related noise.
> >> > >
> >> > > I applied a 1 Hz high-pass filter, but the data still shows a very
> >> large
> >> > > proportion of high-amplitude values. Overall, about *55% of samples
> >> have
> >> > > absolute values greater than 100 µV*, which seems too high for usable
> >> > EEG.
> >> > >
> >> > > My question is: *how to clean this dataset*, since standard filtering
> >> > does
> >> > > not seem sufficient?
> >> > >
> >> > > 1. Percent of samples with |amplitude| > 100 µV per channel:
> >> > > Channel PercentAbs > 100uV
> >> > > FP1 92.946
> >> > > FP2 31.312
> >> > > C3 37.702
> >> > > C4 34.826
> >> > > P7 77.775
> >> > > P8 50.242
> >> > > O1 85.518
> >> > > O2 52.286
> >> > > F7 27.337
> >> > > F8 42.444
> >> > > F3 43.282
> >> > > F4 34.779
> >> > > T7 25.874
> >> > > T8 27.634
> >> > > P3 87.468
> >> > > P4 69.978
> >> > >
> >> > > 2. Additional channel statistics:
> >> > > Channel Min Max Mean Std P5 P95
> >> > > FP1 -1.55E+05 3.98E+05 -0.33667 18173 -18654 14161
> >> > > FP2 -3312.7 3268.7 -0.056876 243.58 -324.27 353.39
> >> > > C3 -2650.7 4902.2 -0.064448 330.28 -465.32 490.06
> >> > > C4 -3082.8 3528.5 -0.052471 269.78 -389.84 426.57
> >> > > P7 -5018 17516 -0.018368 880.09 -1263.1 1431.4
> >> > > P8 -2085.9 9144.2 0.11526 404.97 -523.25 638.35
> >> > > O1 -11920 70997 0.43565 1346 -1509 1743.5
> >> > > O2 -2705.5 9071.8 -0.014948 293.52 -395.37 447.87
> >> > > F7 -2894.7 2943.4 0.024095 193.53 -234.4 255.67
> >> > > F8 -2973.9 2639.3 0.054757 225.84 -314.76 324.82
> >> > > F3 -4752.6 5627.2 0.08169 517.18 -635.01 672.64
> >> > > F4 -6333.3 3600.2 0.07269 515.99 -392.74 630.2
> >> > > T7 -2638.4 2738 0.015915 177.67 -229.59 253.66
> >> > > T8 -3213.5 3242.7 0.0090257 223.3 -298.77 327.22
> >> > > P3 -27780 43690 0.80651 2552.2 -3342.1 3681.6
> >> > > P4 -14668 46330 0.17228 1622.3 -1771.8 1753.2
> >> > > 3. Channel Data Screenshot:
> >> > >
> >> >
> >> https://urldefense.com/v3/__https://drive.google.com/file/d/1FwlMrvYE1e7zn0mQOYnwG7WtYk1Q3SAh/view?usp=sharing__;!!Mih3wA!CrjzKaf8yxIzQ7S7-hNrGlTX3i4E6i-3GXMBj9uYu71UDEIrHRdtyhGKF0ShjK6kP-r-OnCRDllzIN6CQI-UPeoMk6Wj$
> >> > >
> >> > > Thank you so much for your guidance!
> >> > >
> >> > > Best Regards,
> >> > > Jiongjiong
> >> > > _______________________________________________
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