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Designing Closed-Loop Brain-Machine Interfaces Using Model Predictive Control

Electrical and Systems Engineering, Washington University in St. Louis, 1 Brookings Drive, St. Louis, MO 63130, USA
Chemical and Biomolecular Engineering, Lehigh University, 111 Research Drive, Bethlehem, PA 18015, USA
Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
Neurobiology and Anatomy, University of Rochester Medical Center, Rochester, NY 14642, USA
School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710071, China
Department of Automation, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in 2013 American Control Conference, Washington, DC, USA, 17–19 June 2013 and 2015 American Control Conference, Chicago, IL, USA, 1–3 July 2015.
Academic Editor: Mikhail A. Lebedev
Received: 26 February 2016 / Revised: 21 May 2016 / Accepted: 15 June 2016 / Published: 22 June 2016
(This article belongs to the Special Issue Brain-Machine Interface Technology)
Brain-machine interfaces (BMIs) are broadly defined as systems that establish direct communications between living brain tissue and external devices, such as artificial arms. By sensing and interpreting neuronal activities to actuate an external device, BMI-based neuroprostheses hold great promise in rehabilitating motor disabled subjects, such as amputees. In this paper, we develop a control-theoretic analysis of a BMI-based neuroprosthetic system for voluntary single joint reaching task in the absence of visual feedback. Using synthetic data obtained through the simulation of an experimentally validated psycho-physiological cortical circuit model, both the Wiener filter and the Kalman filter based linear decoders are developed. We analyze the performance of both decoders in the presence and in the absence of natural proprioceptive feedback information. By performing simulations, we show that the performance of both decoders degrades significantly in the absence of the natural proprioception. To recover the performance of these decoders, we propose two problems, namely tracking the desired position trajectory and tracking the firing rate trajectory of neurons which encode the proprioception, in the model predictive control framework to design optimal artificial sensory feedback. Our results indicate that while the position trajectory based design can only recover the position and velocity trajectories, the firing rate trajectory based design can recover the performance of the motor task along with the recovery of firing rates in other cortical regions. Finally, we extend our design by incorporating a network of spiking neurons and designing artificial sensory feedback in the form of a charged balanced biphasic stimulating current. View Full-Text
Keywords: closed-loop brain-machine interfaces; artificial sensory feedback; model predictive control closed-loop brain-machine interfaces; artificial sensory feedback; model predictive control
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MDPI and ACS Style

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.

AMA Style

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.

Chicago/Turabian Style

Kumar, 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.

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