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ASME Press Select Proceedings
International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
By
ISBN:
9780791859902
No. of Pages:
1400
Publisher:
ASME Press
Publication date:
2011
eBook Chapter
207 Classifying EEG-Based Motor Imagery Tasks by Means of Wavelet Packet and Sample Entropy
By
Kefang Gao
,
Kefang Gao
College of Mechanical and Electrical Engineering,
fujian Agriculture and Forestry University
, Fuzhou, Fujian 350002
.; cdkfgao@tom.com
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Xiang Zhang
Xiang Zhang
College of Mechanical and Electrical Engineering,
fujian Agriculture and Forestry University
, Fuzhou, Fujian 350002
.; zj990302@189.cn
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Page Count:
4
-
Published:2011
Citation
Gao, K, & Zhang, X. "Classifying EEG-Based Motor Imagery Tasks by Means of Wavelet Packet and Sample Entropy." International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011). Ed. Ming, C. ASME Press, 2011.
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A method of classification based on wavelet packet and Sample Entropy was proposed to improve the correct classification rates of mental task EEG signal. Firstly, With the help of wavelet package, the original EEG signals are decomposed and then the energy feature related to frequency bands and time were extracted from the original EEGsignals.Thirdly, the energy and sample entropy are used as a vector and the vector was used by BP Neural network to classify EEG-based motor imagery tasks.The results showed that the new method based on the combinatorial feature of the energy and sample entropy which were extracted during...
Abstract
Keywords
Introduction
Feature Extraction
Classification
Conclusion
Acknowledgments
References
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