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Updated: May 12, 2025

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
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基于数据对齐的对抗性防御基准,用于基于EEG的BCI.

Xiaoqing Chen1, Tianwang Jia2, Dongrui Wu1

  • 1Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074 China; Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, 518063 China; Zhongguancun Academy, Beijing, 100080 China.

Neural networks : the official journal of the International Neural Network Society
|May 7, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的基准,用于保护基于脑电图 (EEG) 的脑电脑接口 (BCI) 免受对抗性攻击. 将数据增强,对齐和强有力的训练相结合,可以显著提高BCI的准确性和安全性.

关键词:
敌对的攻击是敌对的攻击.对抗性辩护是对抗性的防御.大脑 计算机接口数据调整数据对齐.电脑脑电图 (EEG) 是一种电脑电图.安全的安全的安全的安全的安全.

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科学领域:

  • 神经科学是一个神经科学.
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 机器学习被广泛用于基于脑电图 (EEG) 的脑电脑接口 (BCI) 中的信号解码.
  • 现有的研究主要侧重于提高BCI准确性,往往忽视了关键的安全方面.
  • 最近的研究强调了基于EEG的BCI对复杂的对抗性攻击的脆弱性.

研究的目的:

  • 为基于EEG的BCI建立第一个对抗防御基准,强调数据对齐.
  • 提高基于EEG的BCI对潜在威胁的准确性和稳定性.
  • 为各种防御战略的有效性提供全面的见解.

主要方法:

  • 对九种对抗性防御方法的评估,包括五种不同的防御策略.
  • 在五个不同的EEG数据集中进行测试,包括三个实验范式.
  • 使用三个不同的神经网络架构和四个实验场景进行分析.

主要成果:

  • 数据增强,数据对齐和强有力的培训的整合明显提高了BCI的准确性和稳定性.
  • 这种综合方法的性能超过了仅使用一个或两个这些技术的性能.
  • 详细了解了基于EEG数据对齐的防御的性能特征.

结论:

  • 数据增强,数据调整和强有力的培训的结合策略为基于EEG的BCI提供了卓越的性能.
  • 开发的基准为创建更准确和更安全的基于EEG的BCI提供了有价值的指导.
  • 解决对抗性漏洞对于未来开发和部署BCI技术至关重要.