Connectivity Estimation

I am new to this forum. I want to know if Brainstorm can be used for generation of synthetic EEG data with known connectivity. Any help would be appreciated.

Thank You

This is one of our goals.
See the draft of the connectivity tutorial:
I'm not sure what is the current status of this tutorials, the actual generation of the signals might still be missing.
@hossein27en: Can you please let us know when it this going to be ready?

See also the simulation tutorial:

Dear Francois

Thank you. In Simulated data (AR model) section of the said tutorial , it is mentioned that you guys have generated simulated with known ground truth with equations to create dependencies. These type of generation is very common. However, you have also mentioned that you have generated signals that have peaks in specific frequency band and then simulate signals with known connectivity. However, this method is not given in your tutorial. I am really looking forward to this implementation. Hoping that @hossein27en is about to finish it.

Hi there, @danishmkhan

Thanks for your question and interest. I'm sorry that the documentation is not finalized yet. I'm working to finish it asap. Meanwhile, regarding your question, we have a transfer function between each pair of nodes. It can be seen as an IIR filter. In our specific case, with a few numbers of nodes and sparse connectivity, you can assume that the final transfer function between nodes 1 and 3 are somehow very similar to the designed AR coefficients, and you can estimate those coefficients based on your filter (transfer function) characteristics.

For instance, you want two picks at (pi/6) and (pi/2), so you put two poles inside the unit circle at those frequencies with the mentioned phases and near to one radius. Then use the Matlab function "zp2tf" to find the corresponding coefficients and use them to simulate the AR process.

It is a little bit tricky but that's the rationale behind it.

Let me know if you have more questions.

  • Hossein