Medical school teaches students the basics of diseases, including the symptoms and biology associated with those diseases. These basics highlight how different symptoms and biological factors are related. In other words: they're models. Why not make these models explicit to teach them more effectively.
Welcome! This blog is a bit of a playground where I'll talk about issues broadly related to clinical neuroengineering. Specifically, in this blog we'll rely heavily on models to illustrate various points and topics broadly related to neuroscience, psychiatry, neurosurgery, neuromedicine, signal processing, inference, estimation, robotics, control theory, etc. etc. I mean it when I say playground...
This site is hosted on my github repo and all posts are actually jupyter notebooks that you can work directly with yourself!
Here, we'll get our hands dirty with a Hopf system that exhibits a pretty cool bifurcation, or fundamental change in qualitative behavior. I'll talk a bit about what a Hopf system is, we'll see examples of one, we'll see its bifurcations, and briefly talk about why the system is an important one for brain modeling and deep brain stimulation (DBS) optimization. Here we go!
In this post, I try to outline why visual inspection of electrophysiology can be very misleading in trying to find correlations with disease. This approach, common in clinical biometrics searches, can give rise to many false negatives. The goal is to promote a systematic approach using modern signal processing tools. Let's dive in
My first post for MinS will be a concept I've been trying to blog-post up for a while. It's still a work in progress, so feedback very welcome. Here we will be focused on some techniques for dimensionality reduction in the context of blind source separation. Take a look.