通过协方差矩阵对齐提升运动想象脑机接口的跨被试性能。
Transferring Spatial Filters via Tangent Space Alignment in Motor Imagery BCIs
- 在黎曼流形上对齐协方差矩阵，构建新共空间模式滤波器。
- 三组数据集验证，小样本下性能显著优于标准CSP。
- 适合数据量少的跨被试脑机接口应用。
我们提出一种改进运动想象脑机接口跨被试性能的方法，通过在黎曼流形上对齐协方差矩阵，并基于此计算新的共空间模式（CSP）空间滤波器。我们探索了多种融合多被试信息的方式，结果表明性能优于标准CSP。在三个数据集上，该方法相比标准CSP有微弱提升；但在训练数据有限时，优势更为显著。
原文摘要 · Abstract (English)
We propose a method to improve subject transfer in motor imagery BCIs by aligning covariance matrices on a Riemannian manifold, followed by computing a new common spatial patterns (CSP) based spatial filter. We explore various ways to integrate information from multiple subjects and show improved performance compared to standard CSP. Across three datasets, our method shows marginal improvements over standard CSP; however, when training data are limited, the improvements become more significant.
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