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Neural signal processing for closed-loop neuromodulation

Abstract

Purpose

The purpose of this article is to provide an overview of the current status of neural signal processing techniques for closed-loop neuromodulation.

Methods

First we described overall structure of closed-loop neuromodulation systems. Then, the techniques for the stimulus artifact removal were explained, and the methods for neural state monitoring and biomarker extraction were described. Finally, the current status of neuromodulation based on neural signal processing was provided in detail.

Results

Closed-loop neuromodulation system automatically adjusts stimulation parameters based on the brain response in real time. Adequate tools for signal sensing and signal processing can be used to obtain meaningful biomarkers reflecting the state of neural systems. Especially, an appropriate neural signal processing technique can optimize the details of stimulation for effective treatment of target disease.

Conclusions

Neural signal-based biomarkers reflecting the pathophysiological statuses of patients are essential for closedloop neuromodulation, and they should be developed from an understanding of the relationship between clinical states and neural signals.

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Correspondence to Kyung Hwan Kim.

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Cha, K.S., Yeo, D. & Kim, K.H. Neural signal processing for closed-loop neuromodulation. Biomed. Eng. Lett. 6, 113–122 (2016). https://doi.org/10.1007/s13534-016-0231-5

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  • DOI: https://doi.org/10.1007/s13534-016-0231-5

Keywords

  • Closed-loop neuromodulation
  • Neural signal processing
  • Deep brain stimulation
  • Treatment
  • Biomarker
  • Local field potential
  • Single unit activity