Analysis Of LVQ In The Context Of Spontaneous EEG Signal Classification

Abstract

OF THESIS ANALYSIS OF LVQ IN THE CONTEXT OF SPONTANEOUS EEG SIGNAL CLASSIFICATION Learning Vector Quantization (LVQ) has proven to be an effective classification procedure. Since its introduction by Kohonen in 1990 several extensions to the basic algorithm have been proposed. This paper investigates what and how LVQ learns in the context of EEG signal classification. LVQ is shown to be comparable with other Neural Network algorithms for the task of classifying electroencephalograph (EEG) signals, yielding approximately 80% classification accuracy for three out of the four subjects tested when differentiating between two different mental tasks. The best classification accuracy was obtained with unnormalized, sixth-order autoregressive, AR(6), coefficients derived from raw, unfiltered EEG signals. The LVQ2.1 algorithm outperformed any of the other traditional LVQ algorithms tested, yielding a slightly higher classification accuracy than the LVQ3 algorithm. The highest classification accu..