Classify EEG of seeing two different objects [resolved]

SnobinSnobin India
edited August 19 in Research

I have started my brain signal capturing project recently with a BCI cyton module, dry electrodes and Mark4 helmet. My objective is to classify the brain signal of seeing two different objects. For that, I have done an experiment with a ball and bottle that appears in every 10 seconds alternatively in the plane background. Unfortunately, I didn't get any spikes or differences in the signal when the object appeared. I repeated the experiment a lot of times and was also done with blue and white colored paper instead of ball and bottle. But, all the time I got the same type of output with some random spikes. But, I am getting a proper spike in the eye opening and closing. However, I am unable to classify the brain signal as well as I didn't know why I was getting only false signals. Are my electrodes not capable of detecting the signal properly ? Are my experiments not good enough to make any spikes? Can you help me to do the experiment properly or do you have any suggestions for me?

Comments

  • wjcroftwjcroft Mount Shasta, CA

    Hi Snobin,

    EEG and BCI systems generally do not do "thought detection" or "object detection". Because the ensembles / networks of neurons that do such operations do not result in clearly distinguished scalp signals.

    What is your goal with your BCI, in terms of how you want to use it in a project? Common BCI paradigms that you can lookup online include: Motor Imagery, P300, Visual Evoked Potentials (SSVEP and cVEP). An example of a cVEP (code based VEP) is the free open source MindAffect project:

    Regards, William

  • What is your goal with your BCI, in terms of how you want to use it in a project?

    Thankyou @wjcroft for your immediate feedback. Actually, I want to classify the signals that are output by the brain when it sees the objects(bottle, ball) and not seeing anything(empty background) with the help of a neural network. Now, I am getting only the same type of output signals with random spikes and it could not be differentiable. Instead of the random signal spikes, I am expecting signal spikes only when the object appears.

  • wjcroftwjcroft Mount Shasta, CA

    I am expecting signal spikes only when the object appears.

    Hi Snobin, please try a web search on some of the BCI paradigms I mentioned above. There is no ability of BCI to detect on "object type". The number of neurons involved in recognizing specific objects is too small to register at the scalp EEG locations. EEG requires large numbers of neurons to fire simultaneously to overcome the poor signal to noise characteristics.

    The P300 'oddball' BCI paradigm, is slightly related to what you are attempting. However as you can see from the previous video, cVEP speed and available features generally exceed P300 BCIs.

    https://en.wikipedia.org/wiki/Oddball_paradigm

    Regards, William

  • wjcroftwjcroft Mount Shasta, CA

    Another page describing P300 oddball, and how such experiments are performed and measured.

    https://backyardbrains.com/experiments/p300

    You can also find related ML pages:

    https://www.google.com/search?q=p300+oddball+machine+learning

    Again, if you are mainly looking for a free open source BCI solution, that operates much faster than P300 or Motor Imagery or SSVEP, consider the MindAffect mentioned previously. It is much superior in many ways.

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