Wenbo Wang, Lin Sun, and Guici Chen


  1. [1] W.Y. Hsu, Brain-computer interface: The next frontier oftelemedicine in human-computer interaction, Telematics andInformatics, 32(1), 2015, 180–192.
  2. [2] L.F. Nicolas-Alanso, R. Corralejo, J. Gomez-Pilar,D. Alvarez, and R. Hornero, Adaptive stacked generalization for multi-class motor imagery-based brain computerinterfaces, IEEE Transactions on Neural Systems andRehabilitation Engineering, 23(4), 2015, 702–712.
  3. [3] A.R. Hassan and M.I.H. Bhuiyan, A decision support systemfor automatic sleep staging from EEG signals using tunableq-factor wavelet transform and spectral features, Journal ofNeuroscience Methods, 271, 2016, 107–118.
  4. [4] W.Y. Hsu and Y.N. Sun, EEG-based motor imagery analysis using weighted wavelet transform features, Journal ofNeuroscience Methods, 176(2), 2009, 310-318.
  5. [5] M.-H. Yang, W.-Z. Chen, and M.-Y. Li, Multiple featureextraction based on ensemble empirical mode decompositionfor motor imagery EEG recognition tasks, Acta AutomaticaSinica, 43(5), 2017, 743–752.
  6. [6] A.S. Aghaei, M.S. Mahanta, and K.N. Plataniotis, Separablecommon spatio-spectral patterns for motor imagery BCI systems, IEEE Transactions on Biomedical Engineering, 63(1),2016, 15-29.
  7. [7] Y. Zhang, G.X. Zhou, J. Jin, X.Y. Wang, and A. Cichocki, Optimizing spatial patterns with sparse filter bandsfor motor-imagery based brain-computer interface, Journalof Neuroscience Methods, 255, 2015, 85-91.
  8. [8] L.H. He, D. Hu, M. Wan, Y. Wen, K.M. Deneen, and M.C.Zhou, Common Bayesian network for classification of EEGbased multiclass motor imagery BCI, IEEE Transactionson Systems, Man, and Cybernetics: Systems, 46(6), 2016,843-854.
  9. [9] M. Niknazar, S.R. Mousavi, B. Vosoughi Vahdat, and M.Sayyah A new framework based on recurrence quantificationanalysis for epileptic seizure detection, IEEE Journal ofBiomedical and Health Informatics, 17(3), 2013, 572–578.
  10. [10] H. Shabani, M. Mikaili, and S.M.R. Noori, Assessment ofrecurrence quantification analysis (RQA) of EEG for development of a novel drowsiness detection system, BiomedicalEngineering Letters, 6(3), 2016, 196–204.
  11. [11] H.-R. Bian, J. Wang, C.-X. Han, B. Deng, X.-L. Wei, andY.-Q. Che, Features extraction from EEG signals induced byacupuncture based on the complexity analysis, Acta PhysicaSinica, 60(11), 2011, 118701.
  12. [12] I. Daubechies, J.F. Lu, and H.T. Wu, Synchrosqueezedwavelet transforms: An empirical mode decomposition-liketool, Applied and Computational Harmonic Analysis, 2(30),2011, 243–261.
  13. [13] A. Mert and A. Akan, Emotion recognition based on time-frequency distribution of EEG signals using multivariatesynchrosqueezing transform, Digital Signal Processing, 81,2018, 106–115.
  14. [14] M.M. Kabir, R. Tafreshi, and D.B. Boivin, Enhancedautomated sleep spindle detection algorithm based on synchrosqueezing, Medical & Biological Engineering & Computing, 53(7), 2015, 635–644.
  15. [15] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.A.Manzagol, Stacked denoising autoencoders: Learning usefulrepresentations in a deep network with a local denoisingcriterion, The Journal of Machine Learning Research, 11,2010, 3371–3408.
  16. [16] J.H. Li, Z. Struzik, L.Q. Zhang, and A. Cichocki, Featurelearning from incomplete EEG with denoising autoencoder,Neurocomputing, 165, 2015, 23–31.
  17. [17] S.-J. Xu, L.-X. Han, and X.-Q. Zeng, Natural images classification and retrieval based on improved SDA, PatternRecognition and Artificial Intelligence, 27(8), 2014, 750–757.
  18. [18] F. Takens, Detecting strange attractors in turbulence. Dynamical systems and turbulence, Lecture Notes in Mathematics, 898, 1981, 366–381.
  19. [19] Z. Yin, J. Li, Y. Zhang, A. Ren, K.M. Von Meneen, and L.Huang, Functional brain network analysis of schizophrenicpatients with positive and negative syndrome based on mutualinformation of EEG time series, Biomedical Signal Processingand Control, 31, 2017, 331–338.
  20. [20] L.Y. Cao, Practical method for determining the minimumembedding dimension of a scalar time series, Physica D, 110,1997, 43–50.
  21. [21] M. Tangermann, K.R. Muller, and A. Aertsen, Review of theBCI competition IV, Frontiers in Neuroscience, 6, 2012, 55.
  22. [22] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, Learning representations by back-propagating errors, Nature,323(6088), 1986, 533–536.
  23. [23] P. Vincent, H. Larochelle, Y. Bengio, and P.A. Manzagol,Extracting and composing robust features with denoisingauto-encoders, Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland: ACM, 2008,1096–1103.
  24. [24] H.X. Wang and D. Xu, Comprehensive common spatial patterns with temporal structure information of EEG data: Minimizing nontask related EEG component, IEEE Transactionson Biomedical Engineering, 59(9), 2012, 2496–2505.
  25. [25] Berlin Brain-Computer Interface, BCI competition IV-finalresults [Online], http://www.bbci.de (accessed Jan. 1, 2016).
  26. [26] M. Li, L. Lin, and S. Jia, Multi-class imagery EEG recognitionbased on adaptive subject-based feature extraction and SVM-BP classifier, IEEE International Conference on Mechatronicsand Automation, Beijing, China, 2011, 1184–1189.
  27. [27] M. Li, L. Lin, S. Jia, K.K. Ang, Z.Y. Chin, C.C. Wang,et al, Filter bank common spatial pattern algorithm on BCIcompetition IV datasets 2a and 2b, Frontiers in Neuroscience,6, 2012, 39.
  28. [28] D. Ren, C. Zhang, S. Ren, Z. Zhang, J.H. Wang, and A.X.Lu, An improved approach of cars for Longjing tea detectionbased on near infrared spectra, International Journal ofRobotics & Automation, 33(1), 2018, 97–103.
  29. [29] F. Qu, D. Ren, J. Wang, Z. Zhang, and L. Meng, An ensemblesuccessive project algorithm for liquor detection using nearinfrared sensor, Sensors, 16(1), 2016, 89.
  30. [30] D. Ren, J. Shen, S. Ren, J.H. Wang, and A.X. Lu, An X-rayfluorescence spectroscopy pretreatment method for detectionof heavy metal content in soil, Spectroscopy and SpectralAnalysis, 38(12), 2018, 3934–3940.
  31. [31] P. Shao, W. Shi, and M. Hao, Indicator-kriging-integratedevidence theory for unsupervised change detection in remotelysensed imagery, IEEE Journal of Selected Topics in AppliedEarth Observations and Remote Sensing, 11(12), 2018, 4649–4663.
  32. [32] D. Yang, A. Lu, and J. Wang, Classification of cooked beef,lamb, and pork using hyperspectral imaging, InternationalJournal of Robotics & Automation, 33(3), 2018, 293301.

Important Links:

Go Back