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深度学习模型作为基于EEG的功能性大脑网络的学习者

Yuxuan Yang1, Yanli Li2

  • 1School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, People's Republic of China.

Journal of neural engineering
|February 26, 2025
PubMed
概括

深度学习模型可以从EEG数据中学习一些功能性大脑网络 (FBN) 连接,但与拓结构作斗争. 建议将FBN方法和深度学习结合在一起的混合方法用于全面的EEG分析.

关键词:
关于EEG数据的数据深度学习模型的深度学习模型功能性大脑网络 功能性大脑网络

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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.
  • 机器学习 机器学习

背景情况:

  • 功能性大脑网络 (FBN) 方法经常与深度学习 (DL) 结合用于脑电图 (EEG) 分析.
  • 目前的方法通常涉及两个步骤的过程:用于特征提取的FBN构建,然后进行DL模型分析.
  • 将FBN结构直接集成到DL模型中,可以实现对EEG表示的端到端学习.

研究的目的:

  • 调查深度学习模型是否可以有效地从EEG数据中学习功能性大脑网络构建的过程.
  • 通过评估DL模型复制FBN矩阵的能力来验证DL模型学习FBN结构的能力.

主要方法:

  • 利用深度学习模型来学习功能性大脑网络 (FBN) 矩阵,这些矩阵来自脑电图 (EEG) 数据.
  • 在两个公共EEG数据集上测试了七个DL模型,以学习四个代表性的FBN矩阵.
  • 使用平均平方误差 (MSE),皮尔森相关系数 (Corr) 和一致性相关系数 (CCC) 评估模型性能.

主要成果:

  • 深度学习模型展示了低MSE和高Corr/CCC的连贯性网络.
  • DL模型捕捉了FBN的总体结构,但很难准确地建模特定区域.
  • 配对的t测试显示,大多数网络的预测和实际网络拓性质 (全球效率,节点度) 之间存在显著差异 (p<0.05).

结论:

  • 深度学习模型可以从EEG数据中学习某些功能性大脑网络 (FBNs) 的连接关系.
  • 目前DL模型很难完全捕捉FBN的内在拓结构.
  • 将传统的FBN方法与深度学习相结合的混合策略对于全面的EEG分析至关重要.