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Thanks for this! I'm doing an undergrad thesis applying TDA to EEG.

Currently I've been trying out using a sublevel set filtration on EEG data using a sliding window. For some reason the total persistence (sum of barcode lengths) can classify seizure vs non-seizure time segments.

Do you have any idea why it's able to do this, or where I can learn more?

I know that seizure events correspond to less determinism, as opposed to more chaos.

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That sounds super interesting! I know that there's a lot of work by Perea and Harer about topological time series analysis out there. Most of it is based on sliding windows to my understanding. I would say that the total persistence is a quantifier of the topological complexity of an object (at least when being evaluated in relation to something else; obviously, we can make it as a large as we want by just scaling our weights accordingly)...

Let's discuss this further offline if you want! Drop me an e-mail and I can rope you into my current research on this topic.

I just sent a message to the email on your website :)

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