通过重建道相关性来增强基于转移学习的SSVEP-BCI的域多样性
IEEE transactions on bio-medical engineering
|September 10, 2024
概括
频道相关性重建 (RCC) 增强了对稳态视觉唤起潜力 (SSVEP) 大脑计算机接口 (BCI) 的转移学习. 这种方法优化了源域数据的利用,提高了对有限的校准数据的分类性能.
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:
- 转移学习,包括预训练和微调,改善了基于稳定状态视觉唤起潜力 (SSVEP) 的脑计算机接口 (BCI) 的深度学习分类,特别是在有限的校准数据下.
- 在预培训期间有效地利用源域中的与任务相关的知识仍然是一个重大挑战.
研究的目的:
- 引入一种有效的数据增强方法,即道相关性重建 (RCC),以优化SSVEP-BCI转移学习中的源域数据的使用.
- 通过增加源域的多样性,提高预训练模型的域概括性.
主要方法:
- RCC通过从源域共变矩阵中概率地混合自身向量矩阵来重建训练样本.
- 这种对道相关性的操纵隐含地合成了新领域,增加了数据多样性.
- 该方法的有效性通过独立于主体和主体适应的分类实验来评估.
主要成果:
- 独立于对象的分类表明,RCC显著改善了未见对象的预训练模型性能.
- 根据对象的适应性分类表明,使用RCC增强的预训练模型进行微调,与使用没有RCC的模型相比,产生了明显更好的结果.
结论:
- 通过优化源域数据利用,RCC有效地提高了转移学习性能.
- 通过RCC增强的转移学习显示出对SSVEP-BCI的实际,现实世界的实施有希望.
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