Analysis and Research on Combination Feature Extraction Method of EEG Singnal

LI Jun-wei; Jason Gu; XIE Yun

doi:doi:10.11648/j.acis.20150302.13

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Analysis and Research on Combination Feature Extraction Method of EEG Singnal

LI Jun-wei, Jason Gu, XIE Yun

Published in Automation, Control and Intelligent Systems (Volume 3, Issue 2)

Received: 29 March 2015 Accepted: 11 April 2015 Published: 21 April 2015

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Abstract

EEG feature extraction problem is studied in this paper. EEG analysis is the core content of the Brain-computer interface technology research. How to effectively extract the reflect people's behavior intention characteristic from EEG signals, it's a hot spot in this neighborhood research. According to the characteristics of EEG signal, the single method of feature extraction can't describe the characteristics of the signal very well. So We have own designed experiment, and put forward a combination feature extraction method, which contains calculation the maximum Lyapunov exponent and use wavelet packet transform to calculate the rhythm average energy with wavelet energy entropy, then, the extract feature vector is inputted into the binary tree support vector machine (SVM) and the extreme learning machine (ELM), respectively. From the recognition result show that, when use the combination method of feature extraction to solve the problem of feature extraction and classification about this subject acquisition EEG, it's feasible and effective. At the same time, it also provides a new thought and method.

Published in	Automation, Control and Intelligent Systems (Volume 3, Issue 2)
DOI	10.11648/j.acis.20150302.13
Page(s)	26-30
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

EEG, The Maximum Lyapunov Index, Wavelet Packet Transform, ELM

References

[1]	J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, T. M. Vaughan, “Brain-Computer Interfaces for Communication and Control”, Clinical Neurophisiology, vol. 113, pp. 767-791, 2002.
[2]	A. Subasi, M. K. Kiymikl, A. Alkan, E. Koklukaya, “Neural network classification of EEG signals by using AR with MLE preprocessing for epileptic seizure detection”, Math Comput Appl, vol. 10(1), pp. 57-70, April 2005.
[3]	D. Cvetkovic, E. D. Ubeyli, I. Cosic, “Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study”, Digital Signal Process Rev J, vol. 18(5), pp. 861-874, September 2008.
[4]	Yang Banghua, Liu Li, Zan Peng, Lu Wenyu, “Wavelet packet-based feature extraction for brain-computer interfaces”, Lect. Notes Comput. Sci., vol. 6330 LNBI(PART 3) , pp.19-26, 2010.
[5]	Saha Anuradha, Konar Amita, Ralescu Anca, Nagar Atulya K., “EEG analysis for olfactory perceptual-ability measurement using a recurrent neural classifier”, IEEE Trans. Human Mach. Syst., vol. 44(6), pp. 717-730, December, 2014.
[6]	F. Shayegh, S. Sadri, R. Amirfattahi, K. Ansari-Asl, “A model-based method for computation of correlation dimension, Lyapunov exponents and synchronization from depth-EEG signals”, Comput. Methods Programs Biomed., vol. 113(1), pp. 323-337, 2014.
[7]	Yang Renhuan, Song Aiguo, Xu Baoguo, “Analysis of EEG basic rhythms based on discrete harmonic wavelet packet transform”, Dongnan Daxue Xuebao, vol. 38(6), pp. 996-999, November 2008.
[8]	Sun Yuge, Ye Ning, Xu Xinhe, “The feature extraction and recognition of EEG based on wavelet entropy and distance”, Chinese Contr. Decis. Conf., CCDC, 2008, pp. 4294-4298
[9]	Chen Shanshan, Meng Qingfang, Zhou Weidong, Yang Xinghai, “Seizure detection in clinical EEG based on multi-feature integration and SVM”, Lect. Notes Comput. Sci., vol. 7996 LNAI, pp. 418-426, 2013.
[10]	Yuan Qi, Zhou Weidong, Li Shufang, Cai Dongmei, “Approach of EEG detection based on ELM and approximate entropy”, Yi Qi Yi Biao Xue Bao, vol. 33(3), pp. 514-519, March 2012.

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APA Style

LI Jun-wei, Jason Gu, XIE Yun. (2015). Analysis and Research on Combination Feature Extraction Method of EEG Singnal. Automation, Control and Intelligent Systems, 3(2), 26-30. https://doi.org/10.11648/j.acis.20150302.13

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ACS Style

LI Jun-wei; Jason Gu; XIE Yun. Analysis and Research on Combination Feature Extraction Method of EEG Singnal. Autom. Control Intell. Syst. 2015, 3(2), 26-30. doi: 10.11648/j.acis.20150302.13

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AMA Style

LI Jun-wei, Jason Gu, XIE Yun. Analysis and Research on Combination Feature Extraction Method of EEG Singnal. Autom Control Intell Syst. 2015;3(2):26-30. doi: 10.11648/j.acis.20150302.13

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@article{10.11648/j.acis.20150302.13,
  author = {LI Jun-wei and Jason Gu and XIE Yun},
  title = {Analysis and Research on Combination Feature Extraction Method of EEG Singnal},
  journal = {Automation, Control and Intelligent Systems},
  volume = {3},
  number = {2},
  pages = {26-30},
  doi = {10.11648/j.acis.20150302.13},
  url = {https://doi.org/10.11648/j.acis.20150302.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20150302.13},
  abstract = {EEG feature extraction problem is studied in this paper. EEG analysis is the core content of the Brain-computer interface technology research. How to effectively extract the reflect people's behavior intention characteristic from EEG signals, it's a hot spot in this neighborhood research. According to the characteristics of EEG signal, the single method of feature extraction can't describe the characteristics of the signal very well. So We have own designed experiment, and put forward a combination feature extraction method, which contains calculation the maximum Lyapunov exponent and use wavelet packet transform to calculate the rhythm average energy with wavelet energy entropy, then, the extract feature vector is inputted into the binary tree support vector machine (SVM) and the extreme learning machine (ELM), respectively. From the recognition result show that, when use the combination method of feature extraction to solve the problem of feature extraction and classification about this subject acquisition EEG, it's feasible and effective. At the same time, it also provides a new thought and method.},
 year = {2015}
}

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TY  - JOUR
T1  - Analysis and Research on Combination Feature Extraction Method of EEG Singnal
AU  - LI Jun-wei
AU  - Jason Gu
AU  - XIE Yun
Y1  - 2015/04/21
PY  - 2015
N1  - https://doi.org/10.11648/j.acis.20150302.13
DO  - 10.11648/j.acis.20150302.13
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 26
EP  - 30
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20150302.13
AB  - EEG feature extraction problem is studied in this paper. EEG analysis is the core content of the Brain-computer interface technology research. How to effectively extract the reflect people's behavior intention characteristic from EEG signals, it's a hot spot in this neighborhood research. According to the characteristics of EEG signal, the single method of feature extraction can't describe the characteristics of the signal very well. So We have own designed experiment, and put forward a combination feature extraction method, which contains calculation the maximum Lyapunov exponent and use wavelet packet transform to calculate the rhythm average energy with wavelet energy entropy, then, the extract feature vector is inputted into the binary tree support vector machine (SVM) and the extreme learning machine (ELM), respectively. From the recognition result show that, when use the combination method of feature extraction to solve the problem of feature extraction and classification about this subject acquisition EEG, it's feasible and effective. At the same time, it also provides a new thought and method.
VL  - 3
IS  - 2
ER  -

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Author Information

LI Jun-wei

Electronic & Information Engineering College, Henan University of Science and Technology, Luoyang Henan, China
Jason Gu

School of Biomedical Engineering, Dalhousie University, Halifax, Canada
XIE Yun

Electronic & Information Engineering College, Henan University of Science and Technology, Luoyang Henan, China

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Plain Text BibTeX RIS

APA Style

LI Jun-wei, Jason Gu, XIE Yun. (2015). Analysis and Research on Combination Feature Extraction Method of EEG Singnal. Automation, Control and Intelligent Systems, 3(2), 26-30. https://doi.org/10.11648/j.acis.20150302.13

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ACS Style

LI Jun-wei; Jason Gu; XIE Yun. Analysis and Research on Combination Feature Extraction Method of EEG Singnal. Autom. Control Intell. Syst. 2015, 3(2), 26-30. doi: 10.11648/j.acis.20150302.13

Copy | Download

AMA Style

LI Jun-wei, Jason Gu, XIE Yun. Analysis and Research on Combination Feature Extraction Method of EEG Singnal. Autom Control Intell Syst. 2015;3(2):26-30. doi: 10.11648/j.acis.20150302.13

Copy | Download

@article{10.11648/j.acis.20150302.13,
  author = {LI Jun-wei and Jason Gu and XIE Yun},
  title = {Analysis and Research on Combination Feature Extraction Method of EEG Singnal},
  journal = {Automation, Control and Intelligent Systems},
  volume = {3},
  number = {2},
  pages = {26-30},
  doi = {10.11648/j.acis.20150302.13},
  url = {https://doi.org/10.11648/j.acis.20150302.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20150302.13},
  abstract = {EEG feature extraction problem is studied in this paper. EEG analysis is the core content of the Brain-computer interface technology research. How to effectively extract the reflect people's behavior intention characteristic from EEG signals, it's a hot spot in this neighborhood research. According to the characteristics of EEG signal, the single method of feature extraction can't describe the characteristics of the signal very well. So We have own designed experiment, and put forward a combination feature extraction method, which contains calculation the maximum Lyapunov exponent and use wavelet packet transform to calculate the rhythm average energy with wavelet energy entropy, then, the extract feature vector is inputted into the binary tree support vector machine (SVM) and the extreme learning machine (ELM), respectively. From the recognition result show that, when use the combination method of feature extraction to solve the problem of feature extraction and classification about this subject acquisition EEG, it's feasible and effective. At the same time, it also provides a new thought and method.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Analysis and Research on Combination Feature Extraction Method of EEG Singnal
AU  - LI Jun-wei
AU  - Jason Gu
AU  - XIE Yun
Y1  - 2015/04/21
PY  - 2015
N1  - https://doi.org/10.11648/j.acis.20150302.13
DO  - 10.11648/j.acis.20150302.13
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 26
EP  - 30
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20150302.13
AB  - EEG feature extraction problem is studied in this paper. EEG analysis is the core content of the Brain-computer interface technology research. How to effectively extract the reflect people's behavior intention characteristic from EEG signals, it's a hot spot in this neighborhood research. According to the characteristics of EEG signal, the single method of feature extraction can't describe the characteristics of the signal very well. So We have own designed experiment, and put forward a combination feature extraction method, which contains calculation the maximum Lyapunov exponent and use wavelet packet transform to calculate the rhythm average energy with wavelet energy entropy, then, the extract feature vector is inputted into the binary tree support vector machine (SVM) and the extreme learning machine (ELM), respectively. From the recognition result show that, when use the combination method of feature extraction to solve the problem of feature extraction and classification about this subject acquisition EEG, it's feasible and effective. At the same time, it also provides a new thought and method.
VL  - 3
IS  - 2
ER  -

Copy | Download