Lecture Announcement

Next week I will be hosting Dr. John Porrill from the University of Sheffield. He studies mathematical models of motor control loops in the cerebellum and tries to apply these to solving sophisticated robotics control problems. By nature, this work is multidisciplinary and is applicable to researchers in fields such as engineering, computer science, neuroscience, statistics, and biology.

Dr. Porrill will be giving a lecture on Thursday 2/3 at 12:30pm in Temple's engineering building, room 126. All are welcome!

Paying attention in class

This Doonesbury comic just about sums up the mysteries of undergraduate teaching! (click the thumbnail below for the full strip)

Lecture 2 - Acquiring Bioelectrical Signals

A custom integrated circuit that I designed in grad school for conditioning neural signals.

This week's class will be focused on the problem of how one acquires bioelectrical signals from the body. This necessitates an understanding of electrodes and their interactions with the body's electrolytes, as well as analog signal conditioning and digitization. We will use the brain machine interface as an example data acquisition system, since the concepts involved are fairly general and are hence applicable in other domains. Readings include chapters 1 and 3 from my dissertation(!) as well as Chapter 5 from the venerable book "Medical Instrumentation" by Webster.

Here are the discussion topics I emailed to the class:

Dissertation Chapter 1
What are the things you typically have to do when making a biological recording? Describe the pathway from electrode to computer. What are the details along the way that affect design constraints for the engineer?

Dissertation Chapter 3
You can focus your reading on sections 3.1 - 3.3.
Relate the design that I present in this chapter to the general design constraints laid out in Chapter 1. How do the properties of the neural signals I'm trying to capture impact the design of the data acquisition hardware?

Webster Chapter 5 (Electrode/Electrolyte Interface)
Sections 5.6-5.9 can be skimmed (or skipped if you're pressed for time)
What is the electrode/electrolyte interface? How do the chemical processes involved drive electrode designs? What are polarized and non-polarized electrodes? What are the pros and cons of each? What is motion artifact and how do we protect against it? What is the circuit model for the electrode/electrolyte interface and what does it tell us?

Complex Conjugates

From xkcd.com

EEG Brain Machine Interface

A couple of years ago, I mentored a Senior Design team whose project was to create an EEG-based Brain Machine Interface. They replicated the methods of Wolpaw and McFarland (2004) that were published in the prestigious Proceedings of the National Academy of Science. At the time it was published, that article was quite cutting edge, and yet only three years later, my seniors managed to replicate a good portion of it. Even more impressive was the fact that, instead of using a sophisticated commercial EEG data acquisition system, my seniors built their own EEG amplifier and digitizer, using the freely available OpenEEG design.

The students were only able to get cursor control working in one dimension (up/down) - they ran out of time to implement left/right. Even getting up/down to work was a pretty impressive accomplishment given the poor signal to noise ratio of the homegrown EEG system.

The video below shows their system doing its thing:



Credit goes to my 2007 Senior Design team: Jesse Krigelman, Matt Brooks, Drew Taylor, and Lee Chavous.

Conference Lecture

This is a talk I gave at the 2010 IEEE Engineering in Medicine and Biology Conference in Buenos Aires, Argentina. The topic is Reconfigurable Embedded System Architecture for Neural Signal Processing:

TEDxPhilly

Some shameless self-promotion ... I gave a TEDx talk recently about engineering the brain machine interface: