About the project :
This project focuses on cognitive brain modeling from experiments with live subjects and the design of brain-inspired assistive systems for human beings with severe motor behavior limitations (e.g. paraplegics) through Brain Machine Interface. For the BMI to work naturally with human, the DDDBMI system would need to provide good models of brain motor control and movement plannings for processing the brain signals along with necessary adaptive algorithms incorporating artificially-created desired signals instead of the actual subject's position which is not present in the case of paraplegics. This project will contribute to the design of BMI systems that assist paraplegics in gaining control of artificial limbs. Read more..
Collaboration:
- Advanced Computing and Information Systems Laboratory (ACIS), University of Florida
- Computational NeuroEngineering Laboratory (CNEL), University of Florida
- Neuroprosthetics Research Group (NRG), University of Florida
Current progress:
Middleware
- Template Program for Closed-loop BMI Study
Adaptive Algorithms
- RLS Filtering Implementation
Top Articles:
Brain-Machine Interface Control via Reinforcement Learning (IEEE EMBS 2007)
Abstract: Abstract We investigate the capabilities of reinforcement learning (RL) to create a brain-machine interface (BMI) that uses Q(?) learning to find the functional mapping between neural activity and intended behavior. This paradigm shift is intended to address the issue of paralyzed and amputee patients whom are physically unable to move, which is necessary to train traditional supervised learning BMIs. We created a RLBMI architecture incorporating a rat behavioral paradigm for prosthetic arm control. The performance results show ‘proof of concept’ that RLBMI can learn the temporal structure of neural signals to control a prosthetic arm.(pdf)
Towards Real-Time Distributed Signal Modeling for Brain Machine Interfaces (ICCS 2007)
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 vivo data and implemented using remote computing resources. (pdf)
Related Readings:
Monkeys Adapt Robot Arm As Their Own (from Science Daily)
Monkeys that learn to use their brain signals to control a robotic arm are not just ... > full story
Model Train Controlled via Brain Machine Interface(from Pink Tentacle)
Hitachi has successfully tested a brain-machine interface that allows users to turn power ... > full story