Designing Closed-Loop Brain-Machine Interfaces Using Model Predictive Control †
Abstract
:1. Introduction
2. Psycho-Physiological Cortical Circuit Model of Single Joint Movement
3. Synthetic Experimental Data Generation for Extension Task
4. Wiener and Kalman Filters Based Decoder Designs
4.1. Wiener Filter Based Decoder Design
4.2. Kalman Filter Based Decoder Design
4.3. Comparison of Designed Decoders
5. Need of a Closed-Loop BMI
6. Artificial Proprioceptive Feedback Design
6.1. Model Predictive Control
6.2. Firing Rate Based Closed-Loop BMI Design
6.3. Intracortical Micro-Stimulation Based Closed-Loop BMI Design
7. Discussion
7.1. Tracking Firing Rate of Neurons which Encode Sensory Information Recovers the Natural Performance of BMI
7.2. Generalization beyond Tracking Problems
7.3. Limitations
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
Appendix A.1 Model Parameters for Recurrent Spiking Networks (Equation (23))
Appendix A.2 Particle Swarm Optimization Algorithm
- Position in the -dimensional search space (constraints set) ;
- Best position that it has individually found ;
- Velocity .
Algorithm 1 |
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Algorithm 2 Offline stage |
|
Online stage, for each : |
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Kumar, G.; Kothare, M.V.; Thakor, N.V.; Schieber, M.H.; Pan, H.; Ding, B.; Zhong, W. Designing Closed-Loop Brain-Machine Interfaces Using Model Predictive Control. Technologies 2016, 4, 18. https://doi.org/10.3390/technologies4020018
Kumar G, Kothare MV, Thakor NV, Schieber MH, Pan H, Ding B, Zhong W. Designing Closed-Loop Brain-Machine Interfaces Using Model Predictive Control. Technologies. 2016; 4(2):18. https://doi.org/10.3390/technologies4020018
Chicago/Turabian StyleKumar, Gautam, Mayuresh V. Kothare, Nitish V. Thakor, Marc H. Schieber, Hongguang Pan, Baocang Ding, and Weimin Zhong. 2016. "Designing Closed-Loop Brain-Machine Interfaces Using Model Predictive Control" Technologies 4, no. 2: 18. https://doi.org/10.3390/technologies4020018