Kernel PCA feature extraction of event-related potentials for human signal detection performance.
In: Malmgren, H and Borga, M and Niklasson, L, (eds.)
ARTIFICIAL NEURAL NETWORKS IN MEDICINE AND BIOLOGY.
(pp. 321 - 326).
SPRINGER-VERLAG LONDON LTD
In this paper, we propose the application of the Kernel PCA technique for feature selection in high-dimensional feature space where input variables are mapped by a Gaussian kernel. The extracted features are employed in the regression problem of estimating human signal detection performance from brain event-related potentials elicited by task relevant signals. We report the superiority of Kernel PCA for feature extraction over linear PCA.
|Title:||Kernel PCA feature extraction of event-related potentials for human signal detection performance|
|Event:||Conference on Artificial Neural Networks in Medicine and Biology (ANNIMAB-1)|
|Dates:||2000-05-13 - 2000-05-16|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
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