Todd Coleman had gone from a science and engineering magnet high school in Dallas to earning bachelor's degrees in both computer engineering and electrical engineering at Michigan. What course his educational path would take him in graduate school at MIT he wasn't exactly sure going in, but it's a safe bet he didn't expect to be solving mysteries of the human brain.

"When I went to MIT I didn't know if I was going to go the hard core computer science route or the hard core electrical engineering route," Coleman said.

Coleman, a member of the Human-Computer Intelligent Interaction (HCII) research initiative at the Beckman Institute, chose electrical engineering for his Master's and Ph.D. but his most recent path followed a different course, one geared more toward the human part of the human-computer equation.

Coleman's Master's and Ph.D. thesis advisor at MIT was Muriel Medard, a Professor of Electrical Engineering and Computer Science and former faculty member at Illinois. By the time Coleman earned his Ph.D. from MIT (and the offer of a faculty position at Illinois), Medard was urging him to try something different before starting the life of a professor and researcher.

"She said that would give me more time to mature as a thinker, to work on a completely different class of problems," Coleman said. "So I decided to try and pursue something biological."

That "something biological" turned out to involve neuroscience. Delaying his teaching career at Illinois for a year, Coleman did a postdoctoral stint with Emery Brown, a doctor and nationally-known Professor of Computational Neuroscience from MIT's Department of Brain and Cognitive Science and the Neuroscience Statistics Research Laboratory at the famed Massachusetts General Hospital. The experiences led him into completely new research areas, ones that fit in well with his appointment as a researcher in HCII's Artificial Intelligence group.

Coleman came to Illinois in 2006 where, as an Assistant Professor of Electrical and Computer Engineering, much of his research involves computational neuroscience, or using statistical and computational approaches to understand brain function. He seeks to understand how the brain represents information by investigating how neuron "spike trains" encode information and has begun to design novel, non-invasive brain-machine interface applications.

"We know that neurons generate these little flickers of energy called action potentials and it's basically the timing at which they generate all these spikes that is carrying all the information," Coleman said. "What I am interested in understanding is how is information about the environment, or information about intent, or information about sound, how is that specifically encoded in the timing of the spike trains and I like to use statistical principles to do that."

Coleman, along with his students and collaborators and Beckman colleagues Tim Bretl and Ed Maclin have begun to address brain-machine-interface problems by using non-invasive EEGs to record the brain's electrical signals of test subjects during task performance. They then create statistical models of these neural datasets and signal processing algorithms that "decode" what the subject's intent was; from these come signals that can be used to guide a cursor to aid someone, for example, in searching for a location on a map, or someday perhaps, in using a prosthetic limb.

Coleman's methods use adaptive querying techniques inspired by data compression, optimal control, and feedback information theory principles that explicitly take into account the user's behavior. This approach - as opposed to most current brain-machine interfaces - includes the user's brain as part of a dynamic system.

"The user, by virtue of the visual feedback from what he sees, is going to control this process by what he thinks," Coleman said. "What we're really trying to espouse is: what are the first principle approaches as to how we can really look at this as a closed-loop dynamical system where there's feedback? Just the whole idea of having the brain in the loop for these brain-machine interfaces opens up so many different opportunities."

Coleman said modeling human cognition in this context is challenging. "It's a non-trivial task for a number of reasons but we're chipping away at it slowly but surely."

Coleman said he was inspired to add a biological/application component to his research by friends from Michigan who were also studying in Boston while he was at MIT.

"They were doing M.D./Ph.D.s or doing biological imaging. They all told me to take this hard core math that I learned earning my Ph.D. and apply it toward helping out mankind," Coleman said. "So I told myself 'well now I have a job waiting on me so let me do something completely different and have some fun."

Coleman said Medard and Brown were also important influences on him.

"Emery and Muriel are two of my closest mentors now," he said. "Both Emery and Muriel are very down-to-earth, normal people who value family and treat people as human beings. Everyone calls them on a first name basis; the human component is very strong in both of them and I admire them for that."