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使用CogRepLKNet与EEG-fMRI探索认知工作负载识别.

Yang Shao1, Yueying Zhou2, Xuyun Wen1

  • 1College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, Jiangsu, China.

Neural networks : the official journal of the International Neural Network Society
|January 16, 2026
PubMed
概括
此摘要是机器生成的。

使用多式EEG-fMRI数据,CogRepLKNet可以准确地识别认知工作负载. 这种新型的大内核CNN有效地集成大脑信号,以更低的复杂性提高性能.

关键词:
认知工作负载识别 (CWR)电脑电图 (EEG) 是一种电脑电图.功能性磁共振成像 (fMRI) 是一种功能性磁共振成像.多式联络是多式联络.可重新参数化的大型内核CNN CNN.

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科学领域:

  • 神经科学是一个神经科学.
  • 机器学习 机器学习
  • 生物医学工程 生物医学工程

背景情况:

  • 认知工作负载识别 (CWR) 在整合EEG和fMRI等多式联络数据方面面临挑战.
  • 生理信号的异质性使CWR的统一特征提取变得复杂.

研究的目的:

  • 开发一种新的深度学习模型,用于准确的多式联络CWR.
  • 解决模拟跨模式关系和从EEG和fMRI数据中提取特征的局限性.

主要方法:

  • 提出了CogRepLKNet,一个通用的可重新参数化的大核卷积神经网络 (CNN).
  • 采用了具有大和小内核CNN和自适应性封闭注意力融合的并行感知分支.
  • 利用输入投影来跨越生理信号进行通用特征提取.

主要成果:

  • 在自建的EEG-fMRI数据集上,CogRepLKNet实现了最先进的性能.
  • 与变压器相比,证明了高效的功能集成,减少了计算复杂性和更少的训练样本.
  • 展示了低训练复杂性和模型的易于移植性.

结论:

  • CogRepLKNet有效地模拟了用于增强CWR的跨模式动态.
  • 该模型为多式联机CWR应用提供了一个有前途的解决方案.
  • 开发的方法促进了先进的脑计算机接口和认知监控.