Help with the Identification and Extraction of PREPs (Pain-Related Evoked Potentials)

Hello everyone,
I am currently working on a project for my bachelor's thesis analyzing EEG data, specifically aiming to identify and extract pain-related evoked potentials (PREPs). Here is a brief overview of what I have done so far and the challenges I am facing:

Steps Taken:
* An 8-channel EEG was recorded at a sampling rate of 250 Hz. The electrode positions used were Fp1, Fz, C3, Cz, C4, Pz, O1, and O2. Stimulation was applied with an electrode on the forearm, delivering impulses at a fixed interval of 1 second.
* Blinks were identified as artifacts using the Fp1 electrode and removed through interpolation based on a threshold (mean + 1.75 standard deviations).
* The signal was bandpass-filtered from 1-30 Hz (as my research indicated that PREPs occur within this frequency range) and notch-filtered at 50 Hz (to remove powerline noise).
* I used the Fz, C3, C4, and Pz electrodes to enhance Cz using the Laplacian method. I have attached images showing my Cz channel ('Cz ohne verstärkung' is the raw one and filtered + enhanced is the plot at the top).

Challenges:
* Synchronization of Cap and Stimuli: A major issue I encountered was the lack of synchronization between the electrode cap and the stimuli. This leads to inconsistencies and makes the reliable identification of PREPs difficult.
* Identification and Extraction of PREPs: I have tried to manually identify the PREPs, but it was not possible. There is still too much noise present with a bandpass filter of 1-30 Hz, and nothing significant can be seen at lower frequencies. I also attempted using a wavelet approach, but the identified peaks are not always clear and consistent. Generally, I haven't fully understood the concept of wavelets, and I might be applying them incorrectly.

I hope someone in the forum can help me overcome these challenges and find effective methods for reliably detecting and extracting PREPs in my data.

Thank you in advance for your support!!
Best regards, Ilo

Comments

  • wjcroftwjcroft Mount Shasta, CA

    Hi Ilayda,

    I've not had direct experience with PREP myself. Most of the comments here are the forum are related to visual evoked potentials (SSVEP, cVEP, etc.)

    Where is your reference electrode and Bias/Ground electrode? Are you using gel or paste? Since these PREP waveforms are located on the motor strip (most experiments using Cz ?), the farther from that location your reference is, the higher the differential voltage. It may be better to use a reference at say Fz or AFz (below Fz), or Fpz, than an ear lobe reference as the ear is so close to the motor strip.

    https://www.acns.org/UserFiles/file/EEGGuideline2Electrodenomenclature_final_v1.pdf

    Also looking at a homunculus might shed light on where the forearm is. And possibly select a 10-20 or 10-10 position right over the forearm area. (Taking into account the left/right reversed motor cortex mapping.)

    re: stimuli synchronization.

    You should consider using the 'external trigger' feature of the Cyton. An optoisolator will disassociate your stimuli signal from the low voltage used by Cyton.

    https://docs.openbci.com/Cyton/CytonExternal/#optoisolation

    Regards, William

  • wjcroftwjcroft Mount Shasta, CA

    This looks relevant, comparing various referencing techniques. The best case was 'mathematically linked' earlobes. You may be able to get away with simple electrically linked earlobe electrodes. (Connecting with a shorting cable the left and right ear.)

    https://pmc.ncbi.nlm.nih.gov/articles/PMC7738344/

  • iloloilolo Germany
    edited December 2024

    Hi William,

    Thank you for your response and the valuable insights. Here is some more information about my setup:

    • I used the OpenBCI earclips for referencing and attached them to the earlobes.
    • My cap consists of dry electrodes (comb electrodes), so no gel or paste was used.
    • I have conducted measurements with 20 volunteers. Since some of them were left-handed and others right-handed, I only stimulated the arm that was not the dominant one.
      Based on that and on literature and recommendations, I decided to use the Cz electrode and used the surrounding electrodes primarily to enhance the Cz signal through the Laplacian method.

    Unfortunately, I have already completed my measurements and do not have the opportunity to add an external trigger at this stage.

  • wjcroftwjcroft Mount Shasta, CA

    Unfortunately your setup had several issues. I would strongly suggest you attempt to do at least a few more measurements. Perhaps coordinate with your professor or instructor who could arrange more session times.

    Which dry electrodes were you using? The passive dry electrodes sold by OpenBCI Shop require CONSIDERABLE pressure to give decent amplitude signals. This is usually done with the Ultracortex headset (spring loaded electrodes) or with velcro bands that are tightened sufficiently. A plain elastic EEG cap is unlikely to provide the pressure required. This is why almost all ERP experiments use electrodes with electrolyte (gel or paste) to provide optimal signal levels. Do you have a more normal EEG cap intended for gel usage?

    As you can see from the graphs I inserted above, ERP signals are very weak, say from 5 to 10 microvolts. This is going to be lost in the noise unless you have tight synchronization with the onset of the stimulation time. This is why most ERP work is done with a synchronized trigger being recorded simultaneously.

    re: earlobes reference, you still did not clarify where your Bias/Ground was located? Was it on one earlobe and reference on the other? This is not optimal according to the paper link I included in my last comment. They used linked ears and Ground at another location, usually on the midline such as AFz.

    Best regards, William

  • wjcroftwjcroft Mount Shasta, CA

    The tutorial link below shows how they recover the P300 signal. It requires careful synchronization / alignment with each stimulus, THEN averaging the synced waveforms. This reinforces the desired signal and rejects noise, which is by nature more random.

    https://backyardbrains.com/experiments/p300

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