FEATURE DIMENSIONALITY REDUCTION BY MANIFOLD LEARNING IN BRAIN-COMPUTER INTERFACE DESIGN

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关键词: Principal component analysis 、 Feature (machine learning) 、 Artificial intelligence 、 Manifold alignment 、 Nonlinear dimensionality reduction 、 Pattern recognition 、 Asynchronous communication 、 Interface (computing) 、 Computer science 、 Dimensionality reduction 、 Machine learning 、 Brain–computer interface

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

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FEATURE DIMENSIONALITY REDUCTION BY MANIFOLD LEARNING IN BRAIN-COMPUTER INTERFACE DESIGN

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FEATURE DIMENSIONALITY REDUCTION BY MANIFOLD LEARNING IN BRAIN-COMPUTER INTERFACE DESIGN

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