Clustering Linear Discriminant Analysis for MEG-Based Brain Computer Interfaces

In this paper, we propose a clustering linear discriminant analysis algorithm (CLDA) to accurately decode hand movement directions from a small number of training trials for magnetoencephalography-based brain computer interfaces (BCIs). CLDA first applies a spectral clustering algorithm to automatic...

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Vydané v:	IEEE transactions on neural systems and rehabilitation engineering Ročník 19; číslo 3; s. 221 - 231
Hlavní autori:	Zhang, Jinyin, Sudre, Gustavo, Li, Xin, Wang, Wei, Weber, Douglas J., Bagic, Anto
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.06.2011 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Algorithms Brain - physiology Brain-computer interface (BCI) Cluster Analysis Clustering algorithms Correlation Covariance matrix Data Interpretation, Statistical Decoding Discriminant Analysis Electroencephalography Humans Laplace equations linear discriminant analysis (LDA) Linear Models magnetoencephalography (MEG) Magnetoencephalography - methods Magnetoencephalography - statistics & numerical data Movement - physiology Psychomotor Performance - physiology spectral clustering Studies Training data User-Computer Interface
ISSN:	1534-4320, 1558-0210, 1558-0210
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