FEATURE EXTRACTION OF MOTOR IMAGERY EEG SIGNALS BASED ON MULTI-SCALE RECURRENCE PLOT AND SDA

Wenbo Wang,∗,∗∗,∗∗∗,∗∗∗∗ Lin Sun,∗∗ and Guici Chen∗

References

  1. [1] W.Y. Hsu, Brain-computer interface: The next frontier of telemedicine in human-computer interaction, Telematics and Informatics, 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 computer interfaces, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(4), 2015, 702–712.
  3. [3] A.R. Hassan and M.I.H. Bhuiyan, A decision support system for automatic sleep staging from EEG signals using tunable q-factor wavelet transform and spectral features, Journal of Neuroscience 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 of Neuroscience Methods, 176(2), 2009, 310-318.
  5. [5] M.-H. Yang, W.-Z. Chen, and M.-Y. Li, Multiple feature extraction based on ensemble empirical mode decomposition for motor imagery EEG recognition tasks, Acta Automatica Sinica, 43(5), 2017, 743–752.
  6. [6] A.S. Aghaei, M.S. Mahanta, and K.N. Plataniotis, Separable common 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 bands for motor-imagery based brain-computer interface, Journal of 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 EEG based multiclass motor imagery BCI, IEEE Transactions on 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 quantification analysis for epileptic seizure detection, IEEE Journal of Biomedical and Health Informatics, 17(3), 2013, 572–578.
  10. [10] H. Shabani, M. Mikaili, and S.M.R. Noori, Assessment of recurrence quantification analysis (RQA) of EEG for development of a novel drowsiness detection system, Biomedical Engineering Letters, 6(3), 2016, 196–204.
  11. [11] H.-R. Bian, J. Wang, C.-X. Han, B. Deng, X.-L. Wei, and Y.-Q. Che, Features extraction from EEG signals induced by acupuncture based on the complexity analysis, Acta Physica Sinica, 60(11), 2011, 118701.
  12. [12] I. Daubechies, J.F. Lu, and H.T. Wu, Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool, Applied and Computational Harmonic Analysis, 2(30), 2011, 243–261.
  13. [13] A. Mert and A. Akan, Emotion recognition based on timefrequency distribution of EEG signals using multivariate synchrosqueezing transform, Digital Signal Processing, 81, 2018, 106–115.
  14. [14] M.M. Kabir, R. Tafreshi, and D.B. Boivin, Enhanced automated 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 useful representations in a deep network with a local denoising criterion, The Journal of Machine Learning Research, 11, 2010, 3371–3408.
  16. [16] J.H. Li, Z. Struzik, L.Q. Zhang, and A. Cichocki, Feature learning 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, Pattern Recognition 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 schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series, Biomedical Signal Processing and Control, 31, 2017, 331–338.
  20. [20] L.Y. Cao, Practical method for determining the minimum embedding dimension of a scalar time series, Physica D, 110, 1997, 43–50.
  21. [21] M. Tangermann, K.R. Muller, and A. Aertsen, Review of the BCI 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 denoising auto-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 Transactions on Biomedical Engineering, 59(9), 2012, 2496–2505.
  25. [25] Berlin Brain-Computer Interface, BCI competition IV-final results [Online], http://www.bbci.de (accessed Jan. 1, 2016).
  26. [26] M. Li, L. Lin, and S. Jia, Multi-class imagery EEG recognition based on adaptive subject-based feature extraction and SVMBP classifier, IEEE International Conference on Mechatronics and 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 BCI competition 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 detection based on near infrared spectra, International Journal of Robotics & Automation, 33(1), 2018, 97–103.
  29. [29] F. Qu, D. Ren, J. Wang, Z. Zhang, and L. Meng, An ensemble successive project algorithm for liquor detection using near infrared sensor, Sensors, 16(1), 2016, 89.
  30. [30] D. Ren, J. Shen, S. Ren, J.H. Wang, and A.X. Lu, An X-ray fluorescence spectroscopy pretreatment method for detection of heavy metal content in soil, Spectroscopy and Spectral Analysis, 38(12), 2018, 3934–3940.
  31. [31] P. Shao, W. Shi, and M. Hao, Indicator-kriging-integrated evidence theory for unsupervised change detection in remotely sensed imagery, IEEE Journal of Selected Topics in Applied Earth 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, International Journal of Robotics & Automation, 33(3), 2018, 293301. 9

Important Links:

Go Back