A new architecture for deriving dynamic brain-machine interfaces

Authors:
José Fortes;Renato Figueiredo;Linda Hermer-Vazquez;José Príncipe;Justin C. Sanchez
Affiliations:
Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida;Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida;Dep. of Psychology, University of Florida, Gainesville, Florida;Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida;Dep. of Pediatrics, University of Florida, Gainesville, Florida
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Year:
2006

Citing 2
Cited 2

Multiple paired forward and inverse models for motor control

Neural Networks - Special issue on neural control and robotics: biology and technology
From virtualized resources to virtual computing grids: the In-VIGO system

Future Generation Computer Systems - Special section: Complex problem-solving environments for grid computing

Towards Real-Time Distributed Signal Modeling for Brain-Machine Interfaces

ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Real-time scheduling of mixture-of-experts systems with limited resources

Proceedings of the 13th ACM international conference on Hybrid systems: computation and control

Quantified Score

Hi-index	0.00

Visualization

Abstract

Great potential exists for future Brain Machine Interfaces (BMIs) to help paralyzed patients, and others with motor disabilities, regain (artificial) motor control and autonomy. This paper describes a novel approach towards the development of new design architectures and research test-beds for advanced BMIs. It addresses a critical design challenge in deriving the functional mapping between the subject’s movement intent and actuated behavior. Currently, adaptive signal processing techniques are used to correlate neuronal modulation with known movements generated by the subject. However, with patients who are paralyzed, access to the individual’s movement is unavailable. Inspired by motor control research, this paper considers a predictive framework for BMI using multiple adaptive models trained with supervised or reinforcement learning in a closed-loop architecture that requires real-time feedback. Here, movement trajectories can be inferred and incrementally updated using instantaneous knowledge of the movement target and the individual’s current neuronal activation. In this framework, BMIs require a computing infrastructure capable of selectively executing multiple models on the basis of signals received by and/or provided to the brain in real time. Middleware currently under investigation to provide this data-driven dynamic capability is discussed.