A study of data reduction for P300 speller system

Conference proceedings article

Authors/Editors

Strategic Research Themes

No matching items found.

Publication Details

Author list: Rittikun K., Boonpramuk P., Prechaprapranwong P.

Publisher: Hindawi

Publication year: 2014

ISBN: 9781479929924

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905379754&doi=10.1109%2fECTICon.2014.6839866&partnerID=40&md5=4f696d9afecb3929f2623c308c62991a

Languages: English-Great Britain (EN-GB)

View on publisher site

Abstract

The aim of this research is to reduce data for a P300 spelling system by using downsampling algorithm to reduce the sampling rate of the system. The comparison between this method and standard algorithms such as Discrete Wavelet Transforms (DWT) and Principal Components Analysis (PCA) is also discussed in this paper. While downsampling algorithm is used to reduce sampled data, Ensemble of Support Vector Machine (ESVM) is used to model and to classify the reduced data in order to predict characters. The experimental results show that the accuracy of downsampling algorithm, no-data-reduction algorithm, DWT and PCA are 97.5%, 93.5%, 96.0%, and 79.5% respectively. ฉ 2014 IEEE.

Keywords

amyotrophic lateral sclerosis, discrete wavelet transforms, ensemble of classifiers, P300 speller