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I am an undergraduate student who is trying to classify motor imagery from EEG data!

I have no experience working with EEG or any neuroscience background, I only have a very basic knowledge of how EEG, ERD/ERS and frequency domain work from the papers I've tried to read but don't understand and the basic idea of what they are doing. I also have a reasonably basic knowledge of supervised Machine Learning.

I plan to use ERD/ERS to classify Left foot, Right hand and rest to classify motor imagery in real time!

What is the easiest way to do this?

So far I have bandpassed the data between 7-30 Hz for Mu and Beta wavelengths as they are the ones related to the motor imagery as well as removing the baseline and just plotted the Power Spectral Density. I passed this data through a basic SVM; linear and gaussian, optimised parameters with poor results (60% accuracy on average)

Power Spectral Density

I have no idea how to do feature extraction, where to start, how to properly classify EEG data. and how to do this in real time.

If someone could guide me and provide the steps on how to proceed I would extremely grateful!

I want to work in the field of Brain Computer Interfaces as I find it fascinating, this is my first step :)

Here is the link to the dataset and its details BCI competition: IVc

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  • $\begingroup$ What papers have you read that use techniques similar to what you want to do? $\endgroup$
    – Bryan Krause
    Apr 17 '20 at 15:05
  • $\begingroup$ I have read many referring to Common Spatial Patterns which is too beyond me, I am trying to use simpler methods like Linear Discriminant Analysis, Invariance between data to target ERS and ERD. But in general I'm not sure where to go @BryanKrause $\endgroup$ Apr 20 '20 at 10:02

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