Feature Dimensionality Reduction by Manifold Learning in Brain-computer Interface Design
نویسنده
چکیده
Unsupervised manifold learning for dimensionality reduction has drawn much attention in recent years. This paper applies two manifold learning methods for the first time to feature dimensionality reduction in brain-computer interface (BCI) design, and compares them with principal component analysis (PCA) and supervised PCA that is mathematically equivalent to the common spatial patterns (CSP) method. Their abilities to reveal embedded lowdimensional submanifolds or subspaces of highdimensional BCI data and to preserve or improve the data separability are analysed. Experimental results on asynchronous BCI data from 3 subjects are presented. As the methods are unsupervised, they are particularly suitable for adaptive and asynchronous BCI.
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