On the time series support vector machine using dynamic time warping kernel for brain activity classification

Authors:
W. A. Chaovalitwongse;P. M. Pardalos
Affiliations:
Rutgers University, Piscataway, USA;University of Florida, Gainesville, USA
Venue:
Cybernetics and Systems Analysis
Year:
2008

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Abstract

A new data mining technique used to classify normal and pre-seizure electroencephalograms is proposed. The technique is based on a dynamic time warping kernel combined with support vector machines (SVMs). The experimental results show that the technique is superior to the standard SVM and improves the brain activity classification.