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.
|