[Eeglablist] How to Clean EEG Data with Large Amplitudes After High-Pass Filtering

Wed Jun 24 09:14:07 PDT 2026

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!A98MNmjZFKessU7pK6A85df5rPvWzx7hEV1fL733aQZoclM_LtA5urW7xwm5vP5OPkY0JAsPZwbHn-QZ9iEzpQ$ >

Good luck and all the best,
Tomas

▬▬▬

*Tomas Ros, PhD*

Lecturer, Department of Clinical Neurosciences
CIBM EEG HUG-UNIGE Section
University of Geneva, Switzerland
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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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