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Award Abstract #0540304
DDDAS-TMRP: Dynamic Data-Driven Brain-Machine Interfaces
| NSF Org: |
CNS
Division of Computer and Network Systems
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| Initial Amendment Date: |
September 14, 2005 |
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| Latest Amendment Date: |
September 13, 2007 |
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| Award Number: |
0540304 |
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| Award Instrument: |
Continuing grant |
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| Program Manager: |
Krishna Kant
CNS Division of Computer and Network Systems
CSE Directorate for Computer & Information Science & Engineering
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| Start Date: |
January 1, 2006 |
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| Expires: |
December 31, 2010 (Estimated) |
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| Awarded Amount to Date: |
$954750 |
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| Investigator(s): |
Jose Fortes fortes@ufl.edu (Principal Investigator)
Jose Principe (Co-Principal Investigator)
Renato Figueiredo (Co-Principal Investigator)
Justin Sanchez (Co-Principal Investigator)
Linda Hermer (Co-Principal Investigator)
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| Sponsor: |
University of Florida
1 UNIVERSITY OF FLORIDA
GAINESVILLE, FL 32611 352/392-3516
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| NSF Program(s): |
INT'L RES & EDU IN ENGINEERING,
COMPUTER SYSTEMS,
COLLABORATIVE RESEARCH
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| Field Application(s): |
0000912 Computer Science
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| Program Reference Code(s): |
HPCC, 9218, 7481, 7354, 5980, 5946, 5920, 2884
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| Program Element Code(s): |
T627, T252, S058, 7641, 7354, 7298
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ABSTRACT
Two related DDDAS application areas considered in this project are (1) cognitive brain modeling from experiments with live subjects and (2) the design of brain-inspired assistive systems to help human beings with severe motor behavior limitations (e.g. paraplegics) through brain-machine interfaces (BMIs). Simply stated, a BMI uses brain signals to directly control devices such as computers and robots. Today's BMI designs are extremely primitive and are a far cry from the seamless interface between brain and body in animals. In a healthy animal, the brain constantly learns and adapts to the needs of new physical movement, in addition to providing perfectly timed signals to the motor system. In this process, the brain receives and uses sensory feedback to both learn and generate the signals that lead to purposeful motion. In order to inch closer to BMI designs that are of use to humans, better models of brain motor control and movement planning are needed along with the necessary adaptive algorithms and computational architecture needed for their execution in real time. In light of such goals, this project's activities aim to significantly advance the state of the art of BMI research by developing the models, algorithms and computational architecture of dynamically-data-driven BMIs (DDDBMIs) that implement recently proposed advanced brain models of motor control. Achieving this goal in the proposed approaches will also allow to address a chief problem in current BMI research: The fact that paraplegics cannot train their own network models because they cannot move their limbs.
The research on DDDBMI systems conducted under this project is a drastic departure of the conventional BMI paradigm. The control interface architecture is distributed and borrowed from recent models of neurophysiology of movement, which will enable better overall performance. Learning occurs simultaneously for the subject and the control models in a synergistic manner, which requires more powerful adaptation schemes. Selective use of many computational models is the reason why a dynamically data-driven system is needed to provide the computational needs of a DDDBMI. The project interdisciplinary activities are closely intertwined around the development and integration of the DDDBMI components into a platform for BMI research. Research on middleware addresses the need for dynamic aggregation of Grid-resources with Quality-of-Service guarantees, and support for dynamic computation steering. Research on adaptive algorithms focuses on new data models and learning algorithms. Research on brain modeling concentrates on cognitive models of motor control and advancing our understanding of the neurobiology of movement. In the long run, the BMI experimental research platform will have a dual role: it will help validate the brain models under investigation and it will provide insights on to how to design BMIs for use by paraplegic patients.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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(Showing: 1 - 10 of 14)
(Showing: 1 - 14 of 14)
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B. Mahmoudi, J. DiGiovanna, J. C. Principe, and J. C. Sanchez. "Co-Adaptive Learning in Brain-Machine Interfaces," Brain Inspired Cognitive Systems, Sao Luis, Brazil, 2008.
B. Mahmoudi, J. DiGiovanna, J. C. Principe, and J. C. Sanchez. "Neuronal Tuning in a Brain-Machine Interface during Reinforcement Learning," IEEE International Conference of the Engineering in Medicine and Biology Society, 2008, p. 4491.
B. Mahmoudi, J. Digiovanna, J. C. Principe, J. C. Sanchez. "Neuronal Shaping in a Co-Adaptive Brain-Machine Interface," Computational and Systems Neuroscience, Salt Lake City, UT, 2008, p. 170.
J. C. Sanchez, J. C. Principe, J. Digiovanna, and B. Mahmoudi. "Co-Adaptive Brain-Machine Interfaces via Reinforcement Learning," Conference on Computational Neuroscience, Gainesville, FL, 2008, p. 25.
J. C. Sanchez, J. C. Príncipe, T. Nishida, R. Bashirullah, J. G. Harris, and J. Fortes. "echnology and Signal Processing for Brain-Machine Interfaces: The need for beyond the state-of-the-art tools," IEEE Signal Processing Magazine, v.25(1), 2008, p. 29.
J. Digiovanna, B. Mahmoudi, J. Fortes, J. C. Principe, J. C. Sanchez. "o-Adaptive Brain-Machine Interfaces via Reinforcement Learning," NIH Neural Interfaces Conference, Cleveland, OH, 2008.
J. DiGiovanna, B. Mahmoudi, J. Mitzelfelt, J. C. Sanchez, and J. C. Principe. "Brain-Machine Interface Control via Reinforcement Learning," 3rd International IEEE EMBS Conference on Neural Engineering, 2007.
J. DiGiovanna, L. Citi, K. Yoshida, J. Carpaneto, J. C. Principe, J. C. Sanchez, and S. Micera. "Inferring the Stability of LIFE through Chronic Brain-Machine Interface Decoding Performance," IEEE International Conference of the Engineering in Medicine and Biology Society, v.2008, 2008.
J. DiGiovanna, L. Marchal, P. Rattanatamrong, M. Zhao, S. Darmanjian, B. Mahmoudi, J. C. Sanchez, J. C. Principe, L. Hermer-Vazquez, R. Figueiredo, and J. Fortes. "Towards Real-Time Distributed Signal Modeling for Brain Machine Interfaces," International Conference on Computational Science, Dynamic Data Driven Application Systems - DDDAS 2007, v.1, 2007, p. 964-971.
M. Zhao, P. Rattanatamrong, J. DiGiovanna, B. Mahmoudi, R. J. Figueiredo, J. C. Sanchez, J. C. Principe, and J. A. B. Fortes. "BMI Cyberworkstation: Enabling Dynamic Data-Driven Brain-Machine Interface Research through Cyberinfrastructure," IEEE International Conference of the Engineering in Medicine and Biology Society, 2008, p. 646.
N. Dedual, M. C. Ozturk, J. C. Sanchez, and J. C. Principe. "An Associative Memory Readout in ESN for Neural Action Potential Detection," International Joint Conference on Neural Networks, Orlando, Florida, 2007, 2007.
S. Darmanjian, A. R. C. Paiva, J. C. Principe, M. C. Nechyba, J. Wessberg, M. A. L. Nicolelis, and J. C. Sanchez. "Hierarchical decomposition of neural data using boosted mixtures of independently coupled hidden markov chains," International Joint Conference on Neural Networks, 2007.
Y. Wang, A. R. C. Paiva, J. C. Principe, and J. C. Sanchez. "A Monte Carlo Sequential Estimation of Point Process Optimum Filtering for Brain Machine Interfaces," International Joint Conference on Neural Networks, 2007.
Y. Wang, J. C. Sanchez, and J. C. Principe. "Information Theoretical Estimators of Tuning Depth and Time Delay for Motor Cortex Neurons," 3rd International IEEE EMBS Conference on Neural Engineering, 2007.
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