The feature extraction and recognition of EEG based on wavelet entropy and distance

The paper is based on the technique of brain-computer interface to investigate the EEG of different mental tasks. The wavelet entropy algorithm is applied to realize the feature extraction for the two mental tasks. The Euclidean distance discriminant is proposed to classify the two metal tasks effic...

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Bibliographic Details
Published in2008 Chinese Control and Decision Conference pp. 4294 - 4298
Main Authors Sun Yuge, Ye Ning, Xu Xinhe
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2008
Subjects
Brain computer interfaces
Brain-Computer Interface
Classification algorithms
EEG
Electroencephalography
Entropy
Euclidean distance
Feature extraction
Recognition
Sun
Wavelet entropy
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Summary:The paper is based on the technique of brain-computer interface to investigate the EEG of different mental tasks. The wavelet entropy algorithm is applied to realize the feature extraction for the two mental tasks. The Euclidean distance discriminant is proposed to classify the two metal tasks efficiently and the result is perfect. The recognition rate is up to 96.97%. The research is valuable and significant in the realization of control and communication based on the mental tasks in BCI.
ISBN:9781424417339
1424417333
ISSN:1948-9439
1948-9447
DOI:10.1109/CCDC.2008.4598140