analyzing EEG for long term memory (Flashbulb memories)

iszee94iszee94 Sri Lanka
edited April 2019 in Research
hi there!

i'm new to openbci and im an undergraduate from University of Colombo School of Computing. i'm doing a research on analyzing EEG data to identify the long term memory types like Flashbulb memories.  im wonder what will be the best board and headset layout for my research.

highly appreciate your reply.

thank you

Comments

  • wjcroftwjcroft Mount Shasta, CA
    edited April 2019
    Iszee, hi.

    Have you looked over some of the research in the area of EEG and memory?


    I don't see any "long term" items popping out in my brief scan, only short term / working / etc. I'd suggest doing a research survey and see what kind of equipment they are using: number of channels, scalp locations, digital signal processing, etc.

    Regards,

    William

  • wjcroftwjcroft Mount Shasta, CA
    Found these,




    ----

    One consideration is that these "flashbulb memories" only occur rarely. Thus will not be available for EEG analysis, since their timeframe is entirely unpredictable.

  • BillhBillh
    Flashbulb memory should be more visually vivid, and so should attenuate alpha recorded with eyes closed more than other memory recall would. In theory at least -- I have no idea whether you could actually show this. Nice potential project though!




  • BillhBillh
    Since all you need to measure for alpha attenuation are two occiptal scalp leads and one or two reference leads on ears or frontal vertex, either Cyton or Ganglion might work. If your advisor wants more than two leads on the scalp go with the Cyton. The default 200 Hz signal rate should be fine. Headset is kind of optional in this situation--you could instead just have subject lie down still and apply the silver leads with the paste until they stick enough. The pointy comb type of leads tend to cut into the scalp too much if they lie in their back.
  • iszee94iszee94 Sri Lanka
    thanks for replies guys. im really appreciate that.

    actually the research goal is to reduce cognitive load of a learner and increase the effectiveness of learning by increasing learning scenarios which help to occurred more vivid memories.(flashbulb memories and aha moments evoke more vivid memories)

    so our research path is like this,
    first we try to identify the retention moments when a person engage in a multimedia scenario like a movie using questionnaire based approach meanwhile we capturing the EEG data.

    then we try to find a correlation between the EEG data and retention moments and build a model to predict the retention moment in the future by only analyzing the EEG data.

    so we can use this in a learning scenario and find the scenes where retention moments evoke and the teacher can increase those types of scenes in his teaching materials which reduce the cognitive load of the learner and increase the learning effectiveness.

    im think im gonna need more channels like 16 or 20. i already got emotive epoc and bitalino. 
    im not having much knowledge in where these kind of processing occurred in a brain and the placing of the electrode.

    also im gonna use unsupervised machine learning classification approach to cluster these EEG data in time series with the answers got from the questionnaire.

    thank you guys. really appreciate your replies

  • BillhBillh
    This might help with electrode placements --  they show a map of the ones used:


    I agree that it might be best in your schenario using more electrodes ans machine learning.

  • iszee94iszee94 Sri Lanka
    i think i have to capture more gamma waves from somatosensory cortex. will it be the best option ? 
  • BillhBillh
    edited April 2019
    Good question. Measuring gamma waves as reproducibly varying in a particular way with a specific cognitive task can be challenging with scalp recordings. Much of the research in that area has used implanted electrodes, where it is much easier to measure gamma in a specific brain region reliably.

    If, as you said above, the EEG is measured while the subject is reading, is the active EEG gamma you will measure the gamma seen while reading and while learning, and the control gamma EEG recorded as reading while not learning? How would you tell the "reading plus flashbulb memory" EEG from "only reading" EEG? Could it instead depend on the content of what is read? Or re-read? 

    Choosing the control state will be important to be sure that the machine learning algorithm is actually tuning to measure what you think you intend to measure.




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