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Acute In Vivo Electrophysiological Recordings of Local Field Potentials and Multi-unit Activity from the Hyperdirect Pathway in Anesthetized Rats
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超参数调整和特征提取用于从亚thalamic核局部场潜力异步动作检测.

Thomas Martineau1, Shenghong He2,3, Ravi Vaidyanathan1,4

  • 1Biomechatronics Group, Department of Mechanical Engineering, Imperial College London, London, United Kingdom.

Frontiers in human neuroscience
|June 9, 2023
PubMed
概括

这项研究引入了贝叶斯优化来改善脑电脑界面 (BCI) 和自适应性深度脑刺激 (DBS) 的大脑状态解码. 该方法优化了超参数,提高了神经退行性疾病的解码性能.

关键词:
贝叶斯优化 (BO) 是一个贝叶斯优化.大脑与计算机接口 (BCI)深度大脑刺激 (DBS) 的方法地方现场潜力 (LFPs)时间频率分析

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科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 机器学习 机器学习

背景情况:

  • 从皮下局部场潜力 (LFPs) 解码大脑状态对于神经退行性疾病治疗和脑计算机接口 (BCI) 至关重要.
  • 目前的LFP解码器性能严重依赖于超参数调整,通常是手动和低效的.
  • 超参数优化的现有方法是有限的,导致代码解码器性能低于最佳.

研究的目的:

  • 引入和评估贝叶斯优化 (BO) 方法用于LFP解码管道中的超参数调整.
  • 将BO方法与现有方法进行比较,以解码帕金森病患者的LFPs的自愿运动.
  • 为了确定解码管道中的单个超参数的相关性.

主要方法:

  • 应用了贝叶斯优化 (BO) 框架来调整功能提取,通道选择,分类和LFP解码中的阶段过渡中的超参数.
  • 将BO方法与五种实时特征提取方法和四种分类器进行了比较.
  • 解码性能被评估异步使用从帕金森病患者的亚体核记录的LFPs.

主要成果:

  • 与初始参数设置相比,贝叶斯优化显著提高了LFP解码器性能.
  • 最好的解码器在参与者中实现了0.74 ± 0.06的最大灵敏度-特异性几何平均值.
  • BO替代模型成功地确定了不同超参数的相关性.

结论:

  • 拟议的BO方法为优化神经解码器中的超参数调整提供了一个有希望的解决方案.
  • 这种方法可以提高自适应性深度大脑刺激 (DBS) 和BCI系统的性能.
  • 这些发现可以指导神经解码器的未来代,以改善临床应用.