Exploiting Temporal Context in High-Resolution Movement-Related EEG Classification
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Date
2011-09
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Společnost pro radioelektronické inženýrství
Abstract
The contribution presents an application of a movement-related EEG temporal development classification which improves the classification score of voluntary movements controlled by closely localized regions of the brain. A dynamic Hidden Markov Model-based (HMM) classifier specifically designed to capture EEG temporal behavior was used. Surprisingly, HMM classifiers are rarely used for BCI design despite of their advantages. Because of this we also experimented with Learning Vector Quantization, Perceptron, and Support Vector Machine classifiers using a feature space which captures the temporal dynamics of the data. The results presented in this work show that HMM achieves the best performance due to an a priori information on physiological behavior of EEG inserted to the HMM classifier. Feature extraction process and problems with classification were analyzed as well. Classification scores of 66.7% – 94.7% were achieved in our experiments.
Description
Citation
Radioengineering. 2011, vol. 20, č. 3, s. 666-676. ISSN 1210-2512
http://www.radioeng.cz/fulltexts/2011/11_03_666_676.pdf
http://www.radioeng.cz/fulltexts/2011/11_03_666_676.pdf
Document type
Peer-reviewed
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Published version
Date of access to the full text
Language of document
en