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基于机器学习的分类来解开EEG对TMS和听觉输入的反应.

Andrea Cristofari1, Marianna De Santis2, Stefano Lucidi2

  • 1Department of Civil Engineering and Computer Science Engineering, "Tor Vergata" University of Rome, 00133 Rome, Italy.

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概括

机器学习有效地区分了大脑记录中的跨唤起的潜能 (TEP) 和听觉唤起的潜能 (AEP). 这种方法有助于隔离纯TEP,即使存在听觉污染.

关键词:
这就是TMS-EEG.电脑脑电图 (EEG) 是一种电脑电图.唤起了潜在的潜力.机器学习是机器学习.神经网络的神经网络的神经网络跨的磁性刺激

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科学领域:

  • 神经科学是一个神经科学.
  • 计算神经科学是一种神经科学.
  • 生物医学工程 生物医学工程

背景情况:

  • 跨磁刺激 (TMS) 与脑电图 (EEG) 结合,可以通过跨唤起的潜能 (TEP) 研究皮质生理学.
  • 来自TMS点击的听觉唤起潜力 (AEP) 可以污染TEP,使得与传统的统计方法差异化具有挑战性.
  • 现有的方法难以可靠地将TEP与AEP分开,特别是在不完美的听觉抑制的情况下.

研究的目的:

  • 研究机器学习算法在区分TEP和AEP方面的有效性.
  • 评估机器学习在听觉污染条件下对TEP进行分类的性能.
  • 评估机器学习在隔离纯 TEP 信号方面的潜力.

主要方法:

  • 利用机器学习算法,在三个条件下对健康受试者的信号进行分类:蒙面TMS (TEPs),不蒙面TMS (TEPs + AEPs) 和单独使用AEPs.
  • 在单个主题和组级别上接受过培训和测试的分类人员.
  • 使用平均值与单项试验TEPs进行分类准确性的比较.

主要成果:

  • 机器学习分类器在单个学科水平上取得了可靠的结果,识别了以前未被检测到的差异.
  • 分类准确性在组级下降,并且在比较三个条件与两个条件时下降.
  • 与单个试验数据相比,使用平均TEP时观察到更高的分类准确性.

结论:

  • 机器学习是一个有前途的工具,可以将TEP从污染AEP中解开.
  • 这项概念验证研究表明了AI在完善神经生理信号分析方面的潜力.
  • 这些发现表明,机器学习可以改善TMS-EEG研究中纯TEP的隔离.