Lecture 3 - Brain Machine Interfaces

In Week 3 we started to get into the specifics of neural engineering - in this case we looked at brain machine interfaces, focussing on work of the Nicolelis Lab at Duke  University. In the first half of the class, we finished discussing Chapter 3 of my dissertation (see my Lecture 2 post). This chapter gives a good general overview of biomedical data acquisition. We discussed technical details such as input impedance, gain, filtering, analog to digital conversion, bit-rates, and spike detection.

Following that, we delved into the very helpful review paper by Lebedev and Nicolelis (2006 Trends in Neurosciences). This paper outlines the different types of Brain Machine Interfaces, discusses their relative strengths, and goes into some detail about the roadblocks moving forwards. These roadblocks are summarized as: implantable data acquisition devices, developing real-time computational algorithms, design realistic artificial prostheses, and incorporating sensorimotor feedback.

Upcoming this week, we will be discussing "Signal Processing Challenges for Neural Prostheses" by Linderman et al., in which we will finally start to delve into some mathematics.