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Kernel PCA feature extraction of event-related potentials for human signal detection performance

Rosipal, R; Girolami, M; Trejo, LJ; (2000) 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

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Abstract

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.

Type: Proceedings paper
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)
Location: GOTHENBURG, SWEDEN
Dates: 2000-05-13 - 2000-05-16
ISBN: 1-85233-289-1
UCL classification: UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/1339675
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