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A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems.

Wright J, Macefield VG, van Schaik A, Tapson JC - Front Neurosci (2016)

Bottom Line: It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices.Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback.The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering and Neuroscience, The MARCS Institute, University of Western Sydney Sydney, NSW, Australia.

ABSTRACT
It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback. However, the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well-established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed-loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems, and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.

No MeSH data available.


Artificial Neural Network. An illustration of a typical ANN topology. An input layer projects to a single hidden layer, which connects to the output layer. Common variations include additional hidden layers and recurrent connections.
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Figure 4: Artificial Neural Network. An illustration of a typical ANN topology. An input layer projects to a single hidden layer, which connects to the output layer. Common variations include additional hidden layers and recurrent connections.

Mentions: The Artificial Neural Network is a data driven approach to classification that in contrast to LDA and other statistical methods does not rely on the assumption of the underlying probability distribution of the system (Zhang, 2000). ANNs are organized in layers, with nodes or neurons connected typically in an input, hidden and output layer structure (Figure 4). There are numerous topologies, but among the most popular is the Multi Layer Perceptron (MLP), a three layer feedforward network. ANNs are trained with the presentation of input data that has been identified as belonging to an output class, and a learning rule is applied to adjust the weights on the connections between the nodes, of which back propagation is the most well-known.


A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems.

Wright J, Macefield VG, van Schaik A, Tapson JC - Front Neurosci (2016)

Artificial Neural Network. An illustration of a typical ANN topology. An input layer projects to a single hidden layer, which connects to the output layer. Common variations include additional hidden layers and recurrent connections.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4940409&req=5

Figure 4: Artificial Neural Network. An illustration of a typical ANN topology. An input layer projects to a single hidden layer, which connects to the output layer. Common variations include additional hidden layers and recurrent connections.
Mentions: The Artificial Neural Network is a data driven approach to classification that in contrast to LDA and other statistical methods does not rely on the assumption of the underlying probability distribution of the system (Zhang, 2000). ANNs are organized in layers, with nodes or neurons connected typically in an input, hidden and output layer structure (Figure 4). There are numerous topologies, but among the most popular is the Multi Layer Perceptron (MLP), a three layer feedforward network. ANNs are trained with the presentation of input data that has been identified as belonging to an output class, and a learning rule is applied to adjust the weights on the connections between the nodes, of which back propagation is the most well-known.

Bottom Line: It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices.Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback.The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering and Neuroscience, The MARCS Institute, University of Western Sydney Sydney, NSW, Australia.

ABSTRACT
It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback. However, the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well-established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed-loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems, and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.

No MeSH data available.