Selection and Classification of Elastic Net Feature with Fused Electroencephalogram Features_Journal of Biomedical Engineering

Authors：

1. School of Science, Yanshan University, Qinhuangdao 066004, China;
2. School of Information Science and Engineer, Yanshan University, Qinhuangdao 066004, China;

Corresponding author：

Keywords：

feature fusion; feature selection; common spatial patterns; elastic net logistic regression; coordinate descent

DOI：

10.7507/1001-5515.20160070

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Abstract Full text Figures/Tables Video References Cited by

Signal classification is a key of brain-computer interface (BCI). In this paper, we present a new method for classifying the electroencephalogram (EEG) signals of which the features are heterogeneous. This method is called wrapped elastic net feature selection and classification. Firstly, we used the joint application of time-domain statistic, power spectral density (PSD), common spatial pattern (CSP) and autoregressive (AR) model to extract high-dimensional fused features of the preprocessed EEG signals. Then we used the wrapped method for feature selection. We fitted the logistic regression model penalized with elastic net on the training data, and obtained the parameter estimation by coordinate descent method. Then we selected best feature subset by using 10-fold cross-validation. Finally, we classified the test sample using the trained model. Data used in the experiment were the EEG data from international BCI Competition Ⅳ. The results showed that the method proposed was suitable for fused feature selection with high-dimension. For identifying EEG signals, it is more effective and faster, and can single out a more relevant subset to obtain a relatively simple model. The average test accuracy reached 81.78%.

Citation： LIJing, WANGJinjia, LIHui. Selection and Classification of Elastic Net Feature with Fused Electroencephalogram Features. Journal of Biomedical Engineering, 2016, 33(3): 413-419. doi: 10.7507/1001-5515.20160070 Copy

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3.	REJER I, LORENZ K. Genetic algorithm and forward method for feature selection in EEG feature space[J]. Journal of Theoretical and Applied Computer Science, 2013, 7(2) :72-82.
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7.	SANNELLI C, DICKHAUS T, HALDER S, et al. On optimal channel configurations for SMR-based brain-computer interfaces[J]. Brain Topography, 2010, 23(2) :186-193.
8.	LIN H, YOUPAN H, LI Y Q, et al. Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG[J]. IEEE Transactions on Biomedical Engineering, Neurocomputing, 2013, 121:423-433.
9.	ARVANEH M, CUNTAI G, and KAI K A, et al. Optimizing the channel selection and classification accuracy in EEG-based BCI[J]. IEEE Transactions on Biomedical Engineering, 2011, 58(6) :1865-1873.
10.	YUAN Y, KYRGYZOW O, WIART J, et al. Subject-specific channel selection for classification of motor imagery electroencephalographic data[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver:2013:1277-1280.
11.	GERMAN R B, PEDRO J G L, JOAQUIN R G, et al. Efficient feature selection and linear discrimination of EEG signals[J]. Neurocomputing, 2013, 115:161-165.
12.	PEDRO J G L, GERMAN R B, JOAQUIN R D. Exploring dimensionality reduction of EEG features in motor imagery task classification[J]. Expert Systems with Applications, 2014, 41(11) :5285-5295.
13.	VUCKOVIC A, SEPULVEDA F. A two-stage four-class BCI based on imaginary movements of the left and the right wrist[J]. Medical Engineering and Physics, 2012, 34(7) :964-971.
14.	LIAO X, YAO D Z, WU D, et al. Combining spatial filters for the classification of single-trial EEG in a finger movement task[J]. IEEE Transactions on Biomedical Engineering, 2007, 54(5) :821-831.
15.	ANDERSON C W, STOLZ E A, SHAMSUNDER S. Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks[J]. IEEE Transactions on Biomedical Engineering, 1998, 45(3) :277-286.
16.	ZOU H, HASTIE T. Regularization and variable selection via the elastic net[J]. Journal of the Royal Statistical Society, 2005, 67(2) :301-320.
17.	FRIDMAN J, HASTIE T, HOFLING H, et al. Pathwise coordinate optimization[J]. The annals of Applied Statistics, 2007, 1(2) :302-332.
18.	CHEOLSOO P, LOONEY D, REHMAN N, et al. Classification of motor imagery BCI using multivariate empirical mode decomposition[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(1) :10-22.

1. WOLPAW J R, BIRBAUMER N, HEETDERKS W J, et al. Brain-computer interface technology:A review of the first international meeting[J]. IEEE Transactions on Rehabilitation Engineering, 2000, 8(2) :164-173.
2. REJER I. EEG feature selection for BCI based on motor imaginary task[J]. Foundations of Computing and Decision Sciences, 2012, 37(4) :282-292.
3. REJER I, LORENZ K. Genetic algorithm and forward method for feature selection in EEG feature space[J]. Journal of Theoretical and Applied Computer Science, 2013, 7(2) :72-82.
4. COELHO G P, BARBANTE C C, BOCCATO L, et al. Automatic feature selection for BCI:an analysis using the Davies-Bouldin index and extreme[C]//International Joint Conference on Neural Networks. Brisbane:2012:1-8.
5. BHATTACHARYYA S, SENGUPTA A, CHAKRABORTI T, et al. Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata[J]. Medical and Biological Engineering and Computing, 2014, 52(2) :131-139.
6. ATYABI A, LUERSSEN M, FITZGIBBON N, et al. Evolutionary feature selection and electrode reduction for EEG classifcation[C]//IEEE Congress on Evolutionary Computation. Brisbane:, 2012:1-8.
7. SANNELLI C, DICKHAUS T, HALDER S, et al. On optimal channel configurations for SMR-based brain-computer interfaces[J]. Brain Topography, 2010, 23(2) :186-193.
8. LIN H, YOUPAN H, LI Y Q, et al. Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG[J]. IEEE Transactions on Biomedical Engineering, Neurocomputing, 2013, 121:423-433.
9. ARVANEH M, CUNTAI G, and KAI K A, et al. Optimizing the channel selection and classification accuracy in EEG-based BCI[J]. IEEE Transactions on Biomedical Engineering, 2011, 58(6) :1865-1873.
10. YUAN Y, KYRGYZOW O, WIART J, et al. Subject-specific channel selection for classification of motor imagery electroencephalographic data[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver:2013:1277-1280.
11. GERMAN R B, PEDRO J G L, JOAQUIN R G, et al. Efficient feature selection and linear discrimination of EEG signals[J]. Neurocomputing, 2013, 115:161-165.
12. PEDRO J G L, GERMAN R B, JOAQUIN R D. Exploring dimensionality reduction of EEG features in motor imagery task classification[J]. Expert Systems with Applications, 2014, 41(11) :5285-5295.
13. VUCKOVIC A, SEPULVEDA F. A two-stage four-class BCI based on imaginary movements of the left and the right wrist[J]. Medical Engineering and Physics, 2012, 34(7) :964-971.
14. LIAO X, YAO D Z, WU D, et al. Combining spatial filters for the classification of single-trial EEG in a finger movement task[J]. IEEE Transactions on Biomedical Engineering, 2007, 54(5) :821-831.
15. ANDERSON C W, STOLZ E A, SHAMSUNDER S. Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks[J]. IEEE Transactions on Biomedical Engineering, 1998, 45(3) :277-286.
16. ZOU H, HASTIE T. Regularization and variable selection via the elastic net[J]. Journal of the Royal Statistical Society, 2005, 67(2) :301-320.
17. FRIDMAN J, HASTIE T, HOFLING H, et al. Pathwise coordinate optimization[J]. The annals of Applied Statistics, 2007, 1(2) :302-332.
18. CHEOLSOO P, LOONEY D, REHMAN N, et al. Classification of motor imagery BCI using multivariate empirical mode decomposition[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(1) :10-22.

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