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Motor Imagery Classification Based on Variable Precision Multigranulation Rough Set and Game Theoretic Rough Set

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Book cover Medical Imaging in Clinical Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 651))

Abstract

In this work classification of motor imagery BCI based on Variable Precision Multigranulation Rough Set and Game theoretic Rough Set is proposed. The efficient classification of motor imagery movements of patients can lead to accurate design of Brain Computer Interface (BCI). Data set are collected from BCI Competition III dataset 3a and BCI competition IV data set I. During acquisition there are several noises that affect classification of Electroencephalogram (EEG) Signal, so pre-processing is carried out with Chebyshev type1 filter between 4–40 Hz in order to remove the noises that may exist in signal. The Daubechies wavelet is used for extraction of features from EEG Signal. Variable Precision Multigranulation Rough Set is applied for classification of EEG Signal. Game theoretic Rough set is applied to determine best combination of \( \alpha \) and \( \beta \) are based on accuracy of Variable Precision Multigranulation Rough Set. An experimental result depicts higher accuracy with Variable Precision Multigranulation Rough Set and Game Theoretic rough set compared to existing technique.

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References

  1. Frolov, A.A., Husek, D., Snasel, V., Bobrov, P., Mokienko, O., Tintera, J., Rydlo, J.: Brain-computer interface based on motor imagery: the most relevant sources of electrical brain activity. In: Proceedings of Soft Computing in Industrial Applications, pp. 1–10 (2012)

    Google Scholar 

  2. Lécuyer, A., Lotte, F., Reilly, R.B., Leeb, R., Hirose, M., Slater, M.: Brain-computer interfaces, virtual reality, and videogames. Computer 10(41), 66–72 (2008)

    Google Scholar 

  3. Rebsamen, Brice, Burdet, Etienne, Guan, Cuntai, Zhang, Haihong, Teo, Chee Leong, Zeng, Qiang, Laugier, Christian, Jr Ang, Marcelo H.: Controlling a wheelchair indoors using thought. Intell. Syst. IEEE 22(2), 18–24 (2007)

    Article  Google Scholar 

  4. Mugler, E.M., Ruf, C.A., Halder, S., Bensch, M., Kubler, A.: Design and implementation of a P300-based brain-computer interface for controlling an internet browser. In: Neural Syst. Rehabil. Eng., IEEE Trans. 18(6), 599–609 (2010)

    Google Scholar 

  5. Lotzea, Martin, Halsband, Ulrike: Motor imagery. J. Physiol. 99, 386–395 (2006)

    Google Scholar 

  6. Grosse-Wentrup, Moritz: Understanding brain connectivity patterns during motor imagery for brain-computer interfacing. Adv. Neural Inf. Process. Syst. (NIPS) 21, 561–568 (2008)

    Google Scholar 

  7. Nicolas-Alonso, L.F., Corralejo, R., Álvarez, D., Hornero, R.: Adaptive classification framework for multiclass motor imagery-based BCI, Chapter XIII. In: Mediterranean Conference on Medical and Biological Engineering and Computing, vol. 41, pp. 762–765 (2013)

    Google Scholar 

  8. Miranda, Eduardo Reck, Brouse, Andrew: Interfacing the brain directly with musical systems: on developing systems for making music with brain signals. Leonardo 38(4), 331–336 (2005)

    Article  Google Scholar 

  9. Cabredo, Rafael, Legaspi, Roberto, Inventado, Paul Salvador, Numao, Masayuki: Discovering emotion-inducing music features using EEG signals. J. Adv. Comput. Intell. Intell. Inform. 17(3), 362–370 (2013)

    Google Scholar 

  10. Renuga Devi, K., Hannah Inbarani, H.: Motor imagery classification based on variable precision multigranulation rough set. Adv. Intell. Syst. Comput. 412, 145–154 (2016)

    Article  Google Scholar 

  11. Sitaram, Ranganatha, Zhang, Haihong, Guan, Cuntai, Thulasidas, Manoj, Hoshi, Yoko, Ishikawa, Akihiro, Shimizu, Koji, Birbaumer, Niels: Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface. NeuroImage 34, 1416–1427 (2007)

    Article  Google Scholar 

  12. Corralejo, Rebeca, Nicolás, Luis F., Alonso, Álvarez, Daniel, Hornero, Roberto: A P300 based brain–computer interface aimed at operating electronic devices at home for severely disabled people. Med. Biol. Eng. Comput. 52, 861–872 (2014)

    Article  Google Scholar 

  13. Kaiser, Vera, Bauernfeind, Günther, Kaufmann, Tobias, Kreilinger, Alex, Kübler, Andrea, Neuper, Christa: Cortical effects of user learning in a motor-imagery BCI training. Int. J. Bioelectromag. 13(2), 60–61 (2011)

    Google Scholar 

  14. Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Braincomputer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)

    Article  Google Scholar 

  15. Velásquez-Martínez, L.F., Álvarez-Meza, A.M., Castellanos-Domínguez, C.G.: Motor imagery classification for BCI using common spatial patterns and feature relevance analysis. Nat. Artif. Comput. Eng. Med. Appl. 7931, 365–374 (2013)

    Google Scholar 

  16. Duan, L., Zhong, H., Miao, J., Yang, Z., Ma, W., Zhang, X.: A voting optimized strategy based on ELM for improving classification of motor imagery BCI data. Cogn. Comput. 6, 477–483 (2014)

    Google Scholar 

  17. Rodrıguez-Bermudez, G., García-Laencina, P.J.: Analysis of EEG signals using nonlinear dynamics and chaos: a review. Appl. Math. Inf. Sci. 9(5), 2309–2321 (2015)

    Google Scholar 

  18. Kubler, A., Kotchoubey, B., Kaiser, J., Wolpaw, R.J., Birbaumer, N.: Brain-computer communication: unlocking the locked in. Psychol. Bull. 127(3), 358–375 (2001)

    Article  Google Scholar 

  19. Calvo-Dmgz, D., Galvez, J.F., Glez-Pena, D., Gomez-Meire, S., Fdez-Riverola, F.: Using variable precision rough set for selection and classification of biological knowledge integrated in DNA gene expression. J. Integr. Bioinf. 9(3), 1–17 (2012)

    Google Scholar 

  20. Wei, Jin-Mao, Wang, Ming-Yang, You, Jun-Ping: VPRSM based decision tree classifier. Comput. Inform. 26, 663–677 (2007)

    MATH  Google Scholar 

  21. Beynon, Malcolm J., Peel, Michael J.: Variable precision rough set theory and data discretisation: an application to corporate failure prediction. Omega 29, 561–576 (2001)

    Article  Google Scholar 

  22. ziarko, Wojciech: Variable precision rough set model. J. Comput. Syst. Sci. 46, 39–59 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  23. Ang, K.K., Chin, Z.Y., Wang, C., Guan, C., Zhang, H.: Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front. Neurosci. 6–39 (2012)

    Google Scholar 

  24. Zhou, H., Wang, Y., Xu, Q., Huang, J., Wu, J.: An improved support vector machine classifier for EEG-based motor imagery classification. Adv. Neural Netw. 5552, 267–275 (2009)

    Google Scholar 

  25. Mohd Tumari, S.Z., Sudirman, R., Ahmad, A.H.: Selection of a suitable wavelet for cognitive memory using electroencephalograph. Signal Eng. 5, 15–19 (2013)

    Google Scholar 

  26. Procházka, A., Mudrová, M., Vyšata, O., Gráfová, L., Araujo, S.P.S.: Computational intelligence in multi-channel EEG signal analysis. Recent Adv. Intell. Eng. Syst. 378, 361–381 (2012)

    Google Scholar 

  27. Deepa, V.B., Thangaraj, P.: A study on classification of EEG data using Filters, (IJACSA) Int. J. Adv. Comput. Sci. Appl. 2(4) (2011)

    Google Scholar 

  28. Kang, D., Zhizeng, L.: A method of denoising multi-channel EEG signals fast based on PCA and DEBSS algorithm. In: International Conference on Computer Science and Electronics Engineering (ICCSEE), vol. 3, pp. 322–326 (2012)

    Google Scholar 

  29. Omerhodzic, I., Avdakovic, S., Nuhanovic, A., Dizdarevic, K.: Energy distribution of EEG signals: EEG signal wavelet-neural network classifier. World Acad. Sci. Eng. Technol. 4, 1–24 (2010)

    Google Scholar 

  30. Sviderskaya, N.E., Bykov, P.V.: EEG spatial organization during intense hyperventilation (Cyclic breathing) EEG correlates of psychovisceral phenomena. Hum. Physiol. 32(3), 270–277 (2006)

    Article  Google Scholar 

  31. Chen, Chih-Wei, Ming-Shaung, Ju, Sun, Yun-Nien, Lin, Chou-Ching K.: Model analyses of visual biofeedback training for EEG-based brain-computer interface. J. Comput. Neurosci. 27, 357–368 (2009)

    Article  MathSciNet  Google Scholar 

  32. Li, Xiaowei, Zhao, Qinglin, Liu, Li, Peng, Hong, Qi, Yanbing, Mao, Chengsheng, Fang, Zheng, Liu, Quanying: Improve affective learning with EEG approach. Comput. Inform. 29, 557–570 (2010)

    Google Scholar 

  33. Yu, L.: EEG de-noising based on wavelet transformation. In: International Conference Bioinformatics and Biomedical Engineering, pp. 1–4 (2009)

    Google Scholar 

  34. Inuiguchi, Masahiro: Attribute reduction in variable precision rough set model. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 14(4), 61–479 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  35. Inuiguchi, Masahiro: Structure-based attribute reduction in variable precision rough set models. J. Adv. Comput. Intell. Intell. Inform. 10(5), 657–665 (2006)

    Google Scholar 

  36. Ningler, Michael, Stockmanns, Gudrun, Schneider, Gerhard, Kochs, Hans-Dieter, Kochs, Eberhard: Adapted variable precision rough set approach for EEG analysis. Artif. Intell. Med. 47, 239–261 (2009)

    Article  Google Scholar 

  37. Kusunoki, Yoshifumi, Inuiguchi, Masahiro: Variable precision rough set model in information tables with missing values. J. Adv. Comput. Intell. Intell. Inform. 15(1), 110–116 (2011)

    Google Scholar 

  38. Gong, Zengtai, Shi, Zhanhong, Yao, Hongxia: Variable precision rough set model for incomplete information systems and its -reducts. Comput. Inform. 31, 1385–1399 (2012)

    MathSciNet  Google Scholar 

  39. Ziarko, W.: A variable precision rough set model. J. Comput. Syst. Sci. 46, 39–59 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  40. Yao, J.T., Herbert, J.P.: A game-theoretic perspective on rough set analysis. J. Posts Telecommun. 20(3), 291–298 (2008)

    MathSciNet  Google Scholar 

  41. Xu, W., Zhang, X., Wang, Q.: A generalized multigranulation rough set approach. ICIC 681–689 (2012)

    Google Scholar 

  42. Yao, Y., Qian, Y., Liang, J., Dang, C.: MGRS: a multigranulation rough set. Inf. Sc. 1–22 (2009)

    Google Scholar 

  43. Wei, W., Liang, J., Qian, Y., Wang, F.: Variable precision multi-granulation rough set. In: IEEE International Conference on Granular Computing, pp. 639–643 (2012)

    Google Scholar 

  44. Azam, Yao, J.T.: Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets. Int. J. Approx. Reason. 55(1), 142–155 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  45. Azam, Nouman, Yao, JingTao: Game-theoretic rough sets for recommender systems. Knowl. Based Syst. 72, 96–107 (2014)

    Article  Google Scholar 

  46. de Vries, Sjoerd, Mulder, Theo: Motor imagery and stroke rehabilitation. A Crit. Discuss. J. Rehabil. Med. 39, 5–13 (2007)

    Article  Google Scholar 

  47. Vaughan, T.M., Heetderks, W.J., Trejo, Lj, Rymer, W.Z., Weinrich, M., Moore, M.M., Kubler, A.: Brain-computer interface technology: a review of the second international meeting. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 94–109 (2003)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Periyar University, Salem, India

    K. Renuga Devi & H. Hannah Inbarani

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  1. K. Renuga Devi
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  2. H. Hannah Inbarani
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Correspondence to K. Renuga Devi or H. Hannah Inbarani .

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Editors and Affiliations

  1. Techno India College of Technology, Durgapur, West Bengal, India

    Nilanjan Dey

  2. Professional Colleges (SRMGPC), Shri Ramswaroop Memorial Group of, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Devi, K.R., Inbarani, H.H. (2016). Motor Imagery Classification Based on Variable Precision Multigranulation Rough Set and Game Theoretic Rough Set. In: Dey, N., Bhateja, V., Hassanien, A. (eds) Medical Imaging in Clinical Applications. Studies in Computational Intelligence, vol 651. Springer, Cham. https://doi.org/10.1007/978-3-319-33793-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-33793-7_7

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-33793-7

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