TRCA-Net:使用TRCA过器来增强SSVEP分类与卷积神经网络的分类
Yang Deng1,2, Qingyu Sun3, Ce Wang4
1School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China.
Journal of neural engineering
|July 3, 2023
概括
本研究介绍了TRCA-Net,这是一个新的算法,集成了基于知识和深度学习的方法,用于增强稳定状态视觉唤起潜力 (SSVEP) 分类. TRCA-Net 改善了信号与噪声的比率,提高了大脑与计算机接口的性能.
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
- 计算机科学 计算机科学
背景情况:
- 基于稳态视觉唤起潜力 (SSVEP) 的脑计算机接口 (BCI) 由于系统的简单性,最小的训练数据需求和高的信息传输速率而受欢迎.
- 目前的SSVEP信号分类依赖于基于知识的方法,如任务相关组件分析 (TRCA) 或深度学习方法.
- 整合这些不同的SSVEP分类方法仍然是性能提升的未开发领域.
研究的目的:
- 开发和评估一个新的算法,TRCA-Net,用于改进SSVEP信号分类.
- 在统一的框架内利用基于知识的TRCA和深度学习模型的优势.
- 提高SSVEP数据的信号噪声比,以实现更有效的BCI通信和控制.
主要方法:
- TRCA-Net使用TRCA来导出空间过器,从SSVEP数据中提取与任务相关的组件.
- 通过TRCA过的特征被重组成新的多通道信号.
- 然后,这些增强的信号被输入到深层卷积神经网络 (CNN) 中进行分类.
主要成果:
- 在两个大型公共基准数据集上,TRCA-Net表现出显著的有效性.
- 与多个受试者的线下和在线实验证实了TRCA-Net方法的稳定性.
- 废除研究表明TRCA-Net与各种CNN骨干的兼容性,提高了它们的性能.
结论:
- 拟议的TRCA-Net为SSVEP分类准确性提供了一个有希望的进步.
- 这种综合方法有可能显著改善BCI在通信和控制中的实际应用.
- 该研究为TRCA-Net算法提供了开源代码,促进了进一步的研究和开发.
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