Brain-Machine Interfaces (BMI)
Designing better interfaces: skin-like electronics
Capabilities for non-invasive measurement of neural signals are
important because they support many critical biomedical applications,
including brain-machine interface paradigms in mobile applications.
Currently, recording neural signals in mobile environments is a
challenge because conventional measurement devices have rigid or mildly
flexible construction and bulky cables for signal conduction.
Technologies of the future must address these drawbacks, through new
ideas that provide ultrathin, conformal designs, with high fidelity and
non-invasive measurement modes. Our research group, in conjunction with
the research group of John Rogers at UIUC, is developing foldable,
stretchable electrode arrays that can non-invasively measure neural
signals (i.e. EEG) without the need for gel. The electrodes rely on
layouts recently developed for silicon electronics that offer linear
elastic responses to applied force, with the capacity to fold, twist and
deform into various curved shapes. Stretchable electronics have the key
advantage that they can wrap arbitrary, curvilinear surfaces and, at
the same time, achieve mechanical properties that approach those of
tissues of the human body (e.g. skin). These capabilities are especially
significant for applications in skin-mounted devices for
electroencephalography (EEG) in mobile environments.
Here, intimate contact enables efficient electrical coupling for high
fidelity measurement. In particular, the signal to noise ratios of
recorded signals benefit from low output impedances between the
electrodes and the skin, enabled by the conformal interface.
Related publications
- D. H. Kim, N. Lu, R. Ma, Y.S. Kim, R.H. Kim, S. W, S. M. Won, H.
Tao, A. Islam, K.J. Yu, T. Kim, R. Chowdhury, M. Ying, L. Xu, J. Wu, M.
Li, H.J. Chung, F. G. Omenetto, Y. Huang, T. P. Coleman, J. A. Rogers,
“Epidermal Electronics”, Science, Aug 12, 2011.
Related press coverage:
A Team Decision-Theory Approach: “agents cooperating to achieve a common goal”
A brain-machine interface is a system comprising a direct
communication pathway between the brain and an external device. Our
research group has developed an interpretation of the BMI 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, are theoretically optimal, and
attain previously un-attainable performance.
Related publications
- C. Omar, A. Akce, M. Johnson, T. Bretl, R. Ma, E. Maclin, M.
McCormick, and T. P. Coleman, “A Feedback Information-Theoretic Approach
to the Design of Brain-Computer Interfaces”, International Journal on Human-Computer Interaction, January 2011.
- R. Ma, N. Aghasadeghi, J. A. Jarzebowski, T. W. Bretl, and T. P.
Coleman, “A Stochastic Control Approach to Optimally Designing
Variable-Sized Menus in P300 Neural Communication Prostheses”, IEEE Trans on Neural Systems and Rehabilitation Engineering (TNSRE), January 2012.
- S. K. Gorantla, and T. P. Coleman, “Equivalence Between Reliable Feedback Communication and Nonlinear Filter Stability”, IEEE International Symposium on Information Theory , August 2011.
- R. Ma, and T. P. Coleman, “Generalizing the Posterior Matching Scheme to Higher Dimensions via Optimal Transportation”, Allerton Conference on Communication, Control, and Computing, September 2011.
- A. Kulkarni and T. P. Coleman, “An Optimizer’s Approach to
Stochastic Control Problems with Non-classical Information Structure”, IEEE International Conference on Decision and Control (CDC), to appear, December 2012
Machine Learning in Dynamic Interacting Networks: Uncovering Causal Influences
Many current viewpoints about how neural processes integrate to
elicit complex brain function posit that populations of neurons in the
human brain are connected to form functionally specialized assemblies.
With the increasing ability to record multiple neural signals at
different brain areas simultaneously, one core issue in neuroscience
research is understanding the mechanistic phenomena and how to analyze
these ensemble recordings and infer the structure of these mechanisms.
One such approach to attempt to understand this mechanistic phenomena is
by using a statistical measure of causality.
The
directed information is an information-theoretic
quantity analogous to mutual information that encodes the fundamental
limits of communication with feedback. It is directional and
non-symmetric. From the viewpoint of a sequential prediction under the
log loss, it can be shown to be philosophically consistent with “Granger
causality”, in that it measures directionality of causality (e.g., X
causing Y) by assessing whether or not past values of X and Y help to
predict the future of Y better than only past values of Y. We have used
the directed information and extended it to the “right” notion of
causality when we record more than two time series simultaneously. By
espousing a coupled dynamical systems and generative model viewpoint, we
show that the “right” measure of causality within a network of many
interacting processes is the “causally conditioned directed
information”.
We have developed provably good estimation algorithms to estimate
these quantities from data and have demonstrated how the network causal
dynamics represent information processing in the brain. In the primary
visual cortex of an awake-behaving monkey, we analyzed simultaneous
spiking and field potential recordings and demonstrate a consistent
change in causal interactions between cells before, during, and after a
visual stimulus evokes a motor response. Our procedure identifies strong
structure in the estimated causal relationships in the spike trains,
the directionality and speed of which is consistent with predictions
made from the wave propagation of simultaneously recorded local field
potentials.
Our approach is applicable to an arbitrary modality and thus can be
applicable to a variety applications, including social networks,
economics, and network security.
Related publications
- C. Quinn, T. P. Coleman, N. Kiyavash, and N. G. Hatsopoulos,
“Estimating the directed information to infer causal relationships in
ensemble neural spike train recordings”, J. Computational Neuroscience, January 2011.
- S. Kim, K. Takahashi, N. Hatsopoulos, and T. P. Coleman,
“Information Transfer Between Neurons in the Motor Cortex Triggered by
Visual Cues”, IEEE Engineering in Medicine and Biology Society Annual Conference , September 2011.
- J. Etesami, N. Kiyavash, and T. P. Coleman, “Learning Minimal Latent Directed Information Trees”, IEEE International Symposium on Information Theory (ISIT), July 2012.
- C. Quinn, N. Kiyavash, and T. P. Coleman, “Efficient Methods to
Compute Optimal Tree Approximations of Directed Information Graphs”, to
appear, IEEE Transactions on Signal Processing .
