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Classification of Heterogeneous EEG Data by Combining Random Forests
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SYSNO ASEP 0319183 Document Type A - Abstract R&D Document Type The record was not marked in the RIV R&D Document Type Není vybrán druh dokumentu Title Classification of Heterogeneous EEG Data by Combining Random Forests Title Klasifikace heterogenních EEG dat kombinováním modelů získaných metodou Random Forests Author(s) Klaschka, Jan (UIVT-O) RID, SAI, ORCID Source Title Program and Abstracts of IASC 2008. - Tokyo : Japanese Society of Computational Statistics, 2008 / Mizuta M. ; Nakano J. - ISBN 978-4-9904445-0-1
S. 160-160Number of pages 9 s. Publication form CD-ROM - CD-ROM Action IASC 2008 Event date 05.12.2008-08.12.2008 VEvent location Yokohama Country JP - Japan Event type WRD Language eng - English Country JP - Japan Keywords EEG classification ; somnolence ; random forests ; combining classifiers Subject RIV FH - Neurology R&D Projects 1F84B/042/520 GA MDS - Ministry of Transport (MD) ME 949 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10300504 - UIVT-O (2005-2011) Annotation The focus of the paper is development of classification models capable of distinguishing, based on electroencephalography (EEG) data, somnolence (sleepiness) from other brain states typically met when driving a car. It is a part of a broader project aimed at prevention of damage caused by drivers' microsleeps. Random Forests (RF) method was chosen, on account of previous experience, as a base classification tool for the classification tasks studied. It is, however, not only routinely used: Various classification models tailored for a specific individual are constructed by combining a RF model derived from the individual's data with a model based on the data of the other suitably selected individuals. Several model combining strategies are described and results of their application on real-life EEG data in an extensive computational experiment are reported. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2009
Number of the records: 1