通过基于浅微调的转移学习来减少SSVEP-BCI的校准工作
Wenlong Ding1, Aiping Liu1, Xingui Chen2
1Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027 China.
Cognitive neurodynamics
|May 29, 2025
概括
ShallOw微调 (SOFT) 减少了对稳态视觉唤起潜力 (SSVEP) 大脑计算机接口 (BCI) 的校准需求. 这种转移学习方法需要更少的数据类别进行微调,提高BCI可行性.
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
- 生物医学工程 生物医学工程
背景情况:
- 转移学习 (TL) 在稳定状态视觉唤起潜力 (SSVEP) 大脑计算机接口 (BCI) 中减少了校准时间.
- 目前的微调方法需要广泛的数据,涵盖所有刺激类别,阻碍了BCI的实际应用.
研究的目的:
- 引入ShallOw微调 (SOFT) 以尽量减少SSVEP-BCI对校准数据的要求.
- 为目标用户提高BCI的效率和可行性.
主要方法:
- 开发了SOFT,一种TL方法结更深层模型层和微调浅层.
- 利用部分类别数据进行微调以捕捉个人特定的低级特征.
主要成果:
- 与端到端和最后层微调相比,SOFT实现了更高的分类准确性.
- 该方法显著减少了所需的校准类别的数量.
结论:
- 通过要求更少的类别,SOFT有效地减少了SSVEP-BCI中的校准工作.
- 这种方法提高了BCI的实用性和可访问性,以便在现实世界中使用.
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