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Open AccessJournal ArticleDOI

Creating the feedback loop: closed-loop neurostimulation.

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TLDR
This review addresses advances to date of the technology per se, but of the strategies to apply neuronal signals to trigger or modulate stimulation systems.
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This article is published in Neurosurgery Clinics of North America.The article was published on 2014-01-01 and is currently open access. It has received 94 citations till now. The article focuses on the topics: Brain stimulation & Deep brain stimulation.

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Citations
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Journal ArticleDOI

A review of level-set methods and some recent applications

TL;DR: This work discusses how to impose boundary conditions at irregular domains and free boundaries, as well as the extension of level-set methods to adaptive Cartesian grids and parallel architectures.
Journal ArticleDOI

Adaptive deep brain stimulation in a freely moving parkinsonian patient

TL;DR: The future of deep brain stimulation (DBS) for Parkinson's disease (PD) lies in new closed‐loop systems that continuously supply the implanted stimulator with new settings obtained by analyzing a feedback signal related to the patient's current clinical condition.
Journal ArticleDOI

Advances in closed-loop deep brain stimulation devices

TL;DR: The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients, however, like other commercial devices, DBS needs to be automated and modernized.
Journal ArticleDOI

Neurostimulation Devices for the Treatment of Neurologic Disorders.

TL;DR: Electric stimulation technologies are evolving after remaining fairly stagnant for the past 30 years, moving toward potential closed-loop therapeutic control systems with the ability to deliver stimulation with higher spatial resolution to provide continuous customized neuromodulation for optimal clinical outcomes.
Journal ArticleDOI

Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond.

TL;DR: It is argued that the success of closed-loop deep brain stimulation based on electrophysiological biomarkers in patients with Parkinson disease could inspire novel treatments for other neuropsychiatric disorders in which symptoms are driven by pathological activity in motor, cognitive and limbic brain networks.
References
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Journal ArticleDOI

Support-Vector Networks

TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Book ChapterDOI

Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.
BookDOI

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.
Related Papers (5)
Trending Questions (1)
Who developed the open and closed feedback loop theory?

The authors of the paper "Creating the feedback loop: closed-loop neurostimulation" developed the theory of open and closed feedback loops.