The Coleman lab works on research problems at the intersection of information theory and neuroscience, which include analyzing brain electrical activity to identify causes and effects and designing more accurate and useful brain/computer interfaces (BCIs).
Analyzing neural activity in the brain is useful not only for understanding how behavioral and cognitive functions are encoded, but also to develop devices that interpret neural activity. Further, these algorithms could be applied to other sorts of information, such as the behavior of human social networks.
The Coleman group’s work on BCIs extends to how users interact with neurally controlled devices and the physical design of biomedical electronics. They have introduced an approach to BCI design that allows the user to provide the BCI feedback on its interpretations of neural activity, which provided impressive accuracy. Further, they collaborated with a group at the University of Illinois on flexible electronic sensors that incorporate power sources and communication components, eliminating the need for connection by wires. These “epidermal electronics,” which can record neural and muscular activity, could be used for an enormous variety of applications, including remote patient monitoring and improved control of prosthetics. Current CEN projects aim to use these devices to control release from light-responsive carriers.
Kim DH, Lu N, Ma R, Kim YS, Kim RH, Wang S, Wu J, Won SM, Tao H, Islam A, Yu KJ, Kim T, Chowdhury R, Ying M, Xu L, Li M, Chung H, Keum H, McCormick M, Liu P, Zhang Y, Omenetto FG, Huang Y, Coleman TP, Rogers JA, Epidermal Electronics Science 2011; 333&(6044): 838-843.
Omar C, Akce A, Johnson M, Bretl T, Ma R, Maclin E, McCormick M, Coleman TP. A Feedback Information-Theoretic Approach to the Design of Brain-Computer Interfaces. International Journal on Human-Computer Interaction 2011; 27 (1): 5–23.
Coleman TP, Lee AH, Medard M, Effros M. Low-Complexity Approaches to Slepian-Wolf Near-Lossless Distributed Data Compression IEEE Transactions on Information Theory 2006; 52 (8):3546-3561.