通过同时空间-能量表示,对主体独立的SSVEP-BCI进行数据增强
IEEE transactions on bio-medical engineering
|January 30, 2026
概括
这项研究介绍了同时空间能量表示 (SSER),一种用于脑电图 (EEG) 脑电脑接口 (BCI) 的新型数据增强方法. 通过更好地捕捉个体EEG信号风格,SSER增强了独立于主体的分类.
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:
- 在脑电图 (EEG) 脑电脑接口 (BCI) 中,主体独立的分类对于广泛采用至关重要.
- 目前的深度学习 (DL) 数据增强方法难以解决EEG信号风格的学科间变异性.
研究的目的:
- 提出一种新的数据增强方法,即同时空间-能量表示 (SSER),以改进EEG-BCI中的主体独立分类.
- 为了提高DL模型对EEG信号中个体特定风格特征的稳定性.
主要方法:
- SSER使用单值分解 (SVD) 来从EEG信号中提取空间和能量表示.
- 这些表示在信号重建过程中混合在各个领域,以生成多种风格.
- 这种方法旨在学习域不变的特征,并提高对风格变化的稳定性.
主要成果:
- 在公共EEG数据集上,SSER的性能优于现有的数据增强技术.
- 该方法在不同的DL模型中表现出强烈的概括性.
- 与30名受试者进行的线下和在线实验证实了SSER的有效性.
结论:
- 通过同时操纵空间和能量表示,SSER提供了EEG信号风格变化的更丰富的表征.
- 该方法显著推进了对EEG-BCI的独立对象分类.
- 这一创新有助于基于EEG的BCI在现实世界中得到更广泛的应用.
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