Research

A brain-computer interface (BCI) is a system comprising a direct communication pathway between the brain and an external device. Our research group has developed an interpretation of the BCI as a system comprising multiple agents cooperating to achieve a common goal. This “team decision theory” viewpoint has led us to leverage insights from feedback information theory and control theory to develop direct brain control systems that are easy to use, and are theoretically optimal. In short, must be communicated to the interface via recorded brain signals – and given feedback from the interface’s dynamics – so that it may help the user accomplish a task. This new interpretation, wed with recent results on implementable, optimal, feedback information theoretic coding schemes (titled “posterior matching schemes”), led us to design brain-machine interface paradigms from decentralized control and feedback information-theoretic perspective. We have shown that this framework has allowed us to design BCIs that can accomplish tasks that previously before were not possible, and/or with previously unachieved performance. For example, we have been able to use recent works of feedback information theory and optimal transport theory to allow for multi-user collaborative BCIs. The essence of the optimal feedback-based posterior matching scheme – to communicate to the decoder information about the message that is “statistically independent of everything the decoder has seen so far” – led to the hypothesis that its structure is intimately related to stochastic control. As such, it indeed is acting like an optimal stochastic controller, over the space of posteriors, or decoder’s beliefs about the message. This work shows that an optimal feedback encoding scheme acts to geometrically “steer” the decoder’s belief towards certainty at the message point. We have been able to extend posterior matching to applications where the message point is in arbitrary dimensions, and use the theory of optimal transport to develop methods to construct recursive and optimal strategies.

• Dynamical systems, ergodicity, and posterior matching

  T. P. Coleman

  IEEE International Symposium on Information Theory (ISIT)

  2017

• An information and control framework for optimizing user-compliant human–computer interfaces

  J. Tantiongloc., D. A. Mesa, R. Ma, S. Kim, C. Gil, J. C. Rosa, V. Manian, and T. P. Coleman

  Proceedings of the IEEE

  2017

• An optimizer's approach to stochastic control problems with nonclassical information structures

  A. Kulkarni and T. P. Coleman

  IEEE Transactions on Automatic Control

  2015

• A feedback information-theoretic approach to the design of brain-computer interfaces

  C. Omar, A. Akce, M. Johnson, T. Bretl, R. Ma, E. Maclin, M. McCormick, and T. P. Coleman

  International Journal on Human-Computer Interaction

  2011

• A stochastic control approach to optimally designing hierarchical flash sets in P300 communication prostheses

  R. Ma, N. Aghasadeghi, J. A. Jarzebowski, T. W. Bretl, and T. P. Coleman

  IEEE Trans on Neural Systems and Rehabilitation Engineering (TNSRE)

  2012

• Equivalence Between Reliable Feedback Communication and Nonlinear Filter Stability

  S. K. Gorantla, and T. P. Coleman

  IEEE International Symposium on Information Theory

  2011

• Generalizing the Posterior Matching Scheme to Higher Dimensions via Optimal Transportation

  R. Ma, and T. P. Coleman

  Allerton Conference on Communication, Control, and Computing

  2011

• A stochastic control viewpoint on 'posterior matching'-style feedback communication schemes

  T. P. Coleman

  IEEE International Symposium on Information Theory (ISIT)

  2009