一个基于域选择和特征对齐的新型双步转移框架,用于解码运动图像.
Guanglian Bai1, Jing Jin1,2, Ren Xu3
1Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China.
Cognitive neurodynamics
|December 23, 2024
概括
转移学习显著减少了使用运动图像 (MI) 的脑计算机接口 (BCI) 的校准时间. 一个新的双步框架通过调整数据和选择最佳源域来提高准确性,从而增强MI-BCI的实际应用.
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
- 机器学习 机器学习
背景情况:
- 使用运动图像 (MI) 的脑计算机接口 (BCI) 需要长时间的校准.
- 转移学习 (TL) 在减少MI-BCI校准时间方面表现有前途.
- 对象数据分布的变化阻碍了MI-BCI中的TL有效性.
研究的目的:
- 为MI-BCI开发一个高效的转移学习框架.
- 为了应对不同学科数据分布所带来的挑战.
- 缩短校准时间,提高MI-BCI的分类准确性.
主要方法:
- 提出了一个双步转移框架,集成数据对齐,源域选择和特征对齐.
- 采用预校准策略 (PS) 进行初始源-目标域调整.
- 使用了一种顺序反向选择方法,并采用双模型策略,以获得最佳的源域匹配.
- 嵌入式过器银行规范化常用空间模式 (FBRCSP) 增强功能提取.
- 应用多元体嵌入式分布对齐 (MEDA) 来改进支向量机 (SVM) 预测.
主要成果:
- 拟议的框架实现了比公共和私人数据集的基线方法更高的平均分类准确度 (84.12%,79.91%,78.45%).
- 计算成本显著降低,与基线相比大约减少了一半.
- 有效地减轻了不同受试者之间不同数据分布的影响.
结论:
- 新的双步转移框架通过优化源域选择和特征对齐来提高MI-BCI性能.
- 这种方法有效地减少了校准时间和计算负载,同时提高了分类准确性.
- 该研究强调了高级TL技术在MI-BCI实际应用中的潜力.
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