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

Wed Jun 24 00:01:07 PDT 2026

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
> > _______________________________________________
> > To unsubscribe, send an empty email to
> eeglablist-unsubscribe at sccn.ucsd.edu or visit
> https://sccn.ucsd.edu/mailman/listinfo/eeglablist .
>