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在P300脑电脑接口中交叉数据集的信号对齐.

Minseok Song1, Daeun Gwon1, Sung Chan Jun2,3

  • 1Department of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea.

Journal of neural engineering
|April 24, 2024
PubMed
概括

信号对齐 (SA) 通过使P300事件相关潜在 (ERP) 信号的数据集到数据集转移学习来提高脑计算机接口 (BCI) 的性能. 这种方法提高了跨范式的可转移性,提高了平均精度.

关键词:
大脑-计算机接口接口交叉数据集交叉数据集与事件相关的潜在事件.信号对齐 信号对齐转移学习转移学习

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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:

  • 转移学习在脑计算机接口 (BCI) 研究中至关重要.
  • 现有的学科对学科转移学习是有限的,对跨数据集或跨范式转移的研究很少.
  • P300事件相关潜在 (ERP) 信号是BCI应用的关键.

研究的目的:

  • 为P300 ERP信号提出一种新的信号对齐 (SA) 方法.
  • 为了实现直观的,计算成本低廉的,有效的跨数据集转移学习.
  • 为了促进BCI的范式向范式转移.

主要方法:

  • 开发了一种线性信号对齐 (SA) 技术.
  • SA使用P300延迟,振幅尺度和反向因子来进行信号转换.
  • 在四个不同的数据集上评估了SA:两个P300拼写BCI,一个面部刺激P300拼写,一个听觉奇怪范式.

主要成果:

  • 通过SA方法,平均精度 (AP) 得分从25.5%提高到35.8%.
  • 平均有36.0%的受试者使用SA.表现有所改善.
  • 使用面部刺激的P300拼写器数据集在不同数据集之间显示出更高的可比性.

结论:

  • 提出了一个简单而直观的ERP信号对齐方法.
  • 证明了跨数据集转移学习的可行性,即使在不同的范式之间.
  • SA增强了BCI模型在各种数据集中的通用性和适用性.