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 feature selection on EEG data for ERD/ERS?
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). From this graph, I'm not sure how I can extract the features that define each catergory.
I don't know how to do feature extraction and where to start learning the basics.
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