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从EEG解码运动动力学,使用可解释的卷积神经网络.

Davide Borra1, Valeria Mondini2, Elisa Magosso3

  • 1Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Cesena Campus, Cesena, Italy.

Computers in biology and medicine
|August 24, 2023
PubMed
概括
此摘要是机器生成的。

一个可解释的卷积神经网络 (ICNN) 从EEG信号中解码手的动作,改善脑机接口 (BCI) 控制. 转移学习减少了校准时间,使BCI更容易获得.

关键词:
大脑与计算机接口 (BCI)深度神经网络是一个神经网络.电脑电图 (EEG) 是一种电脑电图.可解释的神经网络发动机轨迹的解码方法

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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:

  • 基于脑电图 (EEG) 的脑电脑接口 (BCI) 显示了通过手动动学的连续解码来实现直观控制的前景.
  • 深度神经网络 (DNN) 是有效的,但往往缺乏解释性,并且仅限于主体内部解码.

研究的目的:

  • 开发一个可解释的卷积神经网络 (ICNN) 来解码来自EEG的2D手动力学.
  • 评估主题内部和跨主题解码策略,包括转移学习,以减少校准时间.
  • 解释由ICNN学习的光谱和空间EEG特征.

主要方法:

  • 一个ICNN被开发来解码2D手的位置和速度从EEG数据在追踪任务的追踪任务.
  • 该ICNN使用主题内部和跨主题方法进行培训,重点是转移学习.
  • 进行特征相关性分析以解释学习的光谱和空间EEG模式.

主要成果:

  • 与最先进的解码器相比,ICNN表现出更高的性能,提供了性能,尺寸和训练持续时间的最佳平衡.
  • 转移学习显著提高了动力学预测的准确性,特别是在有限的数据.
  • 德尔塔波段在所有受试者中始终具有相关性,其中阿尔法,β和低马波段对一些受试者具有相关性. 感觉运动,视觉和视觉运动区域 (对侧中央和尾部位) 被确定为最重要的区域.

结论:

  • ICNN提供了一种可解释和有效的方法来从EEG解码手动力学,从而推进BCI技术.
  • 该研究强调了转移学习的潜力,以尽量减少BCI校准周期.
  • 可解释的EEG特征分析提供了关于运动控制和BCI性能的神经相关的见解.