TY - GEN
T1 - Advancing neuromodulation using a dynamic control framework
AU - Afshar, Pedram
AU - Wei, Xuan
AU - Lazarewicz, MacIej
AU - Gupta, Rahul
AU - Molnar, Greg
AU - Denison, Timothy
PY - 2011
Y1 - 2011
N2 - The current state of neuromodulation can be cast in a classical dynamic control framework such that the nervous system is the classical plant, the neural stimulator is the controller, tools to collect clinical data are the sensors, and the physician's judgment is the state estimator. This framework characterizes the types of opportunities available to advance neuromodulation. In particular, technology can potentially address two dominant factors limiting the performance of the control system: observability, the ability to observe the state of the system from output measurements, and controllability, the ability to drive the system to a desired state using control actuation. Improving sensors and actuation methods are necessary to address these factors. Equally important is improving state estimation by understanding the neural processes underlying diseases. Development of enabling technology to utilize control theory principles facilitates investigations into improving intervention as well as research into the dynamic properties of the nervous system and mechanisms of action of therapies. In this paper, we provide an overview of the control system framework for neuromodulation, its practical challenges, and investigational devices applying this framework for limited applications. To help motivate future efforts, we describe our chronically implantable, low-power neural stimulation system, which integrates sensing, actuation, and state estimation. This research system has been implanted and used in an ovine to address novel research questions.
AB - The current state of neuromodulation can be cast in a classical dynamic control framework such that the nervous system is the classical plant, the neural stimulator is the controller, tools to collect clinical data are the sensors, and the physician's judgment is the state estimator. This framework characterizes the types of opportunities available to advance neuromodulation. In particular, technology can potentially address two dominant factors limiting the performance of the control system: observability, the ability to observe the state of the system from output measurements, and controllability, the ability to drive the system to a desired state using control actuation. Improving sensors and actuation methods are necessary to address these factors. Equally important is improving state estimation by understanding the neural processes underlying diseases. Development of enabling technology to utilize control theory principles facilitates investigations into improving intervention as well as research into the dynamic properties of the nervous system and mechanisms of action of therapies. In this paper, we provide an overview of the control system framework for neuromodulation, its practical challenges, and investigational devices applying this framework for limited applications. To help motivate future efforts, we describe our chronically implantable, low-power neural stimulation system, which integrates sensing, actuation, and state estimation. This research system has been implanted and used in an ovine to address novel research questions.
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U2 - 10.1109/IEMBS.2011.6090150
DO - 10.1109/IEMBS.2011.6090150
M3 - Conference contribution
C2 - 22254398
AN - SCOPUS:84055211999
SN - 9781424441211
VL - 2011
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 671
EP - 674
BT - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Y2 - 30 August 2011 through 3 September 2011
ER -