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

Authors:
Jack Digiovanna;Loris Marchal;Prapaporn Rattanatamrong;Ming Zhao;Shalom Darmanjian;Babak Mahmoudi;Justin C. Sanchez;José C. Príncipe;Linda Hermer-Vazquez;Renato Figueiredo;José A. Fortes
Affiliations:
Dep. of Biomedical Engineering, University of Florida, Gainesville, Florida, USA;Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA;Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA;Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA;Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA;Dep. of Biomedical Engineering, University of Florida, Gainesville, Florida, USA;Dep. of Pediatrics, University of Florida, Gainesville, Florida, USA;Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA;Dep. of Psychology, University of Florida, Gainesville, Florida, USA, University of Florida, Gainesville, Florida, USA;Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA;Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Year:
2007

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Abstract

New architectures for Brain-Machine Interface communication and control use mixture models for expanding rehabilitation capabilities of disabled patients. Here we present and test a dynamic data-driven (BMI) Brain-Machine Interface architecture that relies on multiple pairs of forward-inverse models to predict, control, and learn the trajectories of a robotic arm in a real-time closed-loop system. A method of window-RLS was used to compute the forward-inverse model pairs in real-time and a model switching mechanism based on reinforcement learning was used to test the ability to map neural activity to elementary behaviors. The architectures were tested with in vivodata and implemented using remote computing resources.