Yao Jun, Dewald Julius P A
Physical Therapy and Human Movement Sciences department, Northwestern University, Chicago, IL 60611, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6473-6. doi: 10.1109/IEMBS.2009.5333583.
This paper focuses on the problem of how time-frequency representation influences the generalization ability of the 'synthesized time-frequency spatial pattern (TFSP)' algorithm in Brain Computer Interface (BCI) for classification. TFSP methods use time-frequency analysis to extract features in both time and frequency domains. Different time-frequency analysis methods have been used before. However, it is still unknown how these different approaches influence the generalization ability. We compared the performance of three different TFSP methods in classifying 3 stroke survivors' intention in hand opening and closing. Each of these TFSP methods uses different time-frequency analysis approaches with different time-frequency resolutions. Our results show that a high resolution in time-frequency resolution doesn't guarantee better generalization ability. It seems that although large redundancy in feature reduces the generalization ability of TFSP method, certain redundancy is necessary for achieving high generalization ability.
本文聚焦于时频表示如何影响脑机接口（BCI）中用于分类的“合成时频空间模式（TFSP）”算法的泛化能力这一问题。TFSP方法使用时频分析在时域和频域中提取特征。此前已使用过不同的时频分析方法。然而，这些不同方法如何影响泛化能力仍不明确。我们比较了三种不同的TFSP方法在对3名中风幸存者手部开合意图进行分类时的性能。这些TFSP方法中的每一种都使用了具有不同时频分辨率的不同时频分析方法。我们的结果表明，时频分辨率高并不保证有更好的泛化能力。似乎虽然特征中的大量冗余会降低TFSP方法的泛化能力，但一定的冗余对于实现高泛化能力是必要的。
