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基于立体脑电图 (SEEG) 的侵入性脑计算机接口的数据增强.

Xiaolong Wu1, Dingguo Zhang1, Guangye Li2

  • 1The Centre for Autonomous Robotics (CENTAUR), Department of Electronic & Electrical Engineering, University of Bath, Bath, United Kingdom.

Journal of neural engineering
|January 18, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的深度学习方法,即基于条件变压器的生成对抗网络 (cTGAN),通过生成现实的数据来改进脑计算机接口 (BCI). cTGAN方法通过有效捕捉立体电脑图 (SEEG) 数据中的时间依赖性来提高分类器的性能.

关键词:
大脑计算机接口 (BCI)数据增强数据增强深度学习是一种深度学习.立体电脑电图 (SEEG) 是一种立体电脑电图.变压器的变压器是一个变压器.

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科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 信号处理 信号处理

背景情况:

  • 深度学习对于脑计算机接口 (BCI) 至关重要,但侵入性BCI的数据有限.
  • 现有的大脑信号数据增强 (DA) 方法往往忽视时间依赖性,依赖于卷积神经网络.

研究的目的:

  • 通过从时间序列角度结合时间关系来增强BCI的生成模型.
  • 引入一种基于条件变压器的新型生成对抗网络 (cTGAN),以改善侵入性BCI中的数据增强.

主要方法:

  • 开发了一个基于条件变压器的生成对抗网络 (cTGAN),以捕捉立体脑电图 (SEEG) 数据中的时间依赖.
  • 根据八名患者的SEEG数据,对cTGAN进行了评估,对 noise injection (NI),变化自编码器 (VAE) 和带梯度惩罚的条件Wasserstein生成对抗网络 (cWGANGP) 进行了评估.
  • 数据质量通过视觉检查,共弦相似性 (CS),詹森-香农距离 (JSD) 和对深度学习分类器性能的影响来评估.

主要成果:

  • cTGAN和cWGANGP生成了现实的SEEG数据,表现优于NI和VAE.
  • cTGAN根据CS和JSD指标生产了优质样本.
  • cTGAN显著提高了深度学习分类器的性能,提高了6%,而cWGANGP的改进率为3.4%.

结论:

  • 这项研究是第一个使用SEEG数据将DA方法应用于侵入性BCI的研究.
  • 拟议的cTGAN证明了保留时间依赖性以提高BCI性能的优势.