Skip to main content
SpringerLink
Log in

Neighborhood based decision theoretic rough set under dynamic granulation for BCI motor imagery classification

  • Original Paper
  • Published:
Journal on Multimodal User Interfaces Aims and scope Submit manuscript

Abstract

Brain Computer Interface is an interesting and important research field that has contributed widespread application systems. In the medical field, it is important for physically challenged persons to aid in rehabilitation and restoration. In Brain Computer Interface, computer acts as interface between brain signals and external device. The computer processes the brain signals and sends necessary instructions to external device. The external device helps in restoring the movement ability of patient. Motor imagery is the imagination of motor movements like hand, foot and tongue. There is an associated brain signal when the normal person moves their hand, foot and tongue. Similarly, there is an associated brain signal when the physically challenged person imagines moving their hand, foot and tongue. When this brain signal is analyzed by brain computer interface, it can facilitate motor movements through external device. The aim of this work is to analyze and classify the brain signals for motor movements to aid in rehabilitation and restoration. In this paper BCI Competition IV Dataset I, Dataset IIa, BCI Competition III Dataset IIIa and Neuroprosthetic EEG Dataset are analyzed A novel optimization technique with Neighborhood Decision Theoretic Rough Set under Dynamic Granulation is proposed for motor imagery classification. Neighborhood based Decision Theoretic Rough Set under Dynamic Granulation (NDTRS under DG) is hybrid approach combining two algorithms Neighborhood Rough Set and Decision Theoretic Rough Set under Dynamic Granulation ((DTRS under DG). Neighborhood Rough Set overcomes the drawback of discretization step in Rough Set. Decision Theoretic Rough Set under Dynamic Granulation algorithm has loss function for classification. The effectiveness of classification is improved since the loss function is involved in the construction of algorithm. The proposed method Neighborhood based Decision Theoretic Rough Set under Dynamic Granulation gives higher classification accuracy compared to existing approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Razi S, Mollaeia MRK, Ghasemi J (2019) A novel method for classification of BCI multi-class motor imagery task based on Dempster–Shafer theory. Information Sci 484:14–26

    Article  Google Scholar 

  2. Kirar JS, Agrawal RK (2019) A combination of spectral graph theory and quantum genetic algorithm to find relevant set of electrodes for motor imagery classification. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2019.105519

    Article  Google Scholar 

  3. Luo J, Wang J, Rong X, Kailiang X (2019) Class discrepancy-guided sub band filter-based common spatial pattern for motor imagery classification. J Neurosci Methods 323:98–107

    Article  Google Scholar 

  4. Li D, Zhang H, Khan MS, Mi F (2018) A self-adaptive frequency selection common spatial pattern and least squares twin support vector machine for motor imagery electroencephalography recognition. Biomed Signal Process Control 42:222–232

    Article  Google Scholar 

  5. Jana GC, Swetapadma A, Pattnaik PK (2018) Enhancing the performance of motor imagery classification to design a robust brain computer interface using feed forward back-propagation neural netpaper. Ain Shams Eng J 9(4):2871–2878

    Article  Google Scholar 

  6. Olivas-Padilla BE, Chacon-Murguia MI (2018) Classification of multiple motor imagery using deep convolutional neural netpapers and spatial filters. Appl Soft Comput J 75:461–472

    Article  Google Scholar 

  7. Dev KR, Inbarani HH (2016) Motor imagery classification based on variable precision multigranulation rough set. Adv Intell Syst Comput 412:145–154

    Google Scholar 

  8. Kumar SU, Inbarani HH (2016) PSO-based feature selection and neighborhood rough set-based classification for BCI multi-class motor imagery task. Neural Comput Appl 28(11):3239–3258

    Article  Google Scholar 

  9. Kang H, Choi S (2014) Bayesian common spatial patterns for multi-subject EEG classification. Neural Netpap 57:39–50

    Article  Google Scholar 

  10. Blankertz B, Müller K-R, Krusienski D, Schalk G, Wolpaw JR, Schlögl A, Pfurtscheller G, del Millán JR, Schröder M, Birbaumer N (2006) The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabilit Eng 14(2):153–159

    Article  Google Scholar 

  11. Blankertz B, Müller K-R, Curio G, Vaughan TM, Schalk G, Wolpaw JR, Schlögl A, Neuper C, Pfurtscheller G, Hinterberger T, Schröder M, Birbaumer N (2004) The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans Biomed Eng 51(6):1044–1051

    Article  Google Scholar 

  12. Tangermann M, Müller K-R, Aertsen A, Birbaumer N, Braun C, Brunner C, Leeb R, Mehring C, Miller KJ, Müller-Putz GR, Nolte G, Pfurtscheller G, Preissl H, Schalk G, Schlögl A, Vidaurre C, Waldert S, Blankertz B (2012) Review of the BCI competition IV. Front Neurosci 6(55):1–31

    Google Scholar 

  13. Wanga J, Feng Z, Lu N, Sun L, Luo J (2018) An information fusion scheme based common spatial pattern method for classification of motor imagery tasks. Biomed Signal Process Control 46:10–17

    Article  Google Scholar 

  14. Szczuko P (2017) Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications. Multimedia Tools Appl 76(24):25697–25711

    Article  Google Scholar 

  15. Pattnaik PK, Sarraf J (2016) Brain computer interface issues on hand movement. J King Saud Univ Comput Inf Sci 30(1):18–24

    Google Scholar 

  16. IanGrout (2008) Introduction to digital signal processing. Digital systems design with FPGAs and CPLDs, pp 475–536

  17. Dwivedi S (2015) Comparison and implementation of different types of IIR filters for lower order and economic rate. Int J Eng Stud Tech Approach 1(10):15–26

    Google Scholar 

  18. ANZ Rashed (2013) Band pass filters with low pass and high pass filters integrated with operational amplifiers in advanced integrated communication circuits. Int J Adv Res Comput Eng Technol (IJARCET), 2(3):861–866, ISSN: 2278–1323

  19. Oppenheim AV, Schafer RW, Buck JR, Discrete–Time Signal Processing, Second edition, ISBN 978-81-317-049209

  20. Van Valkenburg M Analog Filter Design, The Oxford series in Electrical and Computer Engineering, Second edition, ISBN-13: 978-0030592461

  21. Devi KR, Inbarani HH (2016) Motor imagery classification based on variable precision multigranulation rough set and game theoretic rough set. Med Imaging Clin Appl 651:153–174

    Article  MathSciNet  Google Scholar 

  22. Lu H, Eng HL, Guan C, Plataniotis KN, Venetsanopoulos AN (2010) Regularized common spatial pattern with aggregation for EEG classification in small-sample setting. IEEE Trans Biomed Eng 57:2936–2946

    Article  Google Scholar 

  23. Novi Q, Guan C, Dat TH, Xue P (2007) Sub-band common spatial pattern (SBCSP) for Brain computer interface. In: Proceedings of international conference on neural engineering, pp 204–207

  24. Ang KK, Chin ZY, Wang C, Guan C, Zhang H (2012) Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci. https://doi.org/10.3389/fnins.2012.00039

    Article  Google Scholar 

  25. Thomas KP, Guan C, Lau CT, Vinod AP, Ang KK (2009) A new discriminative common spatial pattern method for motor imagery brain-computer interfaces. IEEE Trans Biomed Eng 56:2730–2733

    Article  Google Scholar 

  26. Kumar S, Sharma A (2018) A new parameter tuning approach for enhanced motor imagery EEG signal classification. Med Biol Eng Comput 56:1861–1874

    Article  Google Scholar 

  27. Krishna DH, Pasha IA, Savithri TS (2016) Classification of EEG motor imagery multi class signals based on cross correlation. Procedia Comput Sci 85:490–495

    Article  Google Scholar 

  28. Dean RT, Dunsmuir WTM (2016) Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: the importance of constructing transfer function autoregressive models. Behav Res Methods 48(2):783–802

    Article  Google Scholar 

  29. Antoine J-P, Bogdanova I, Vandergheynst P (2007) The continuous wavelet transform on conic sections. Int J Wavelets Multiresolut Information Process 6:137–156

    Article  MathSciNet  Google Scholar 

  30. Bogdanova I, Vandergheynst P, Antoine J-P, Jacques L, Morvidone M (2005) Stereographic wavelet frames on the sphere. Appl Comput Harmonic Anal 26:223–252

    Article  MathSciNet  Google Scholar 

  31. Bogdanova I, Vandergheynst P, Gazeau J-P (2007) Continuous wavelet transform on the hyperboloid. Appl Comput Harmonic Anal 23:286–306

    Article  MathSciNet  Google Scholar 

  32. Calixto M, Guerrero J (2006) Wavelet Transform on the circle and the real line: a unified group-theoretical treatment. Appl Comput Harmonic Anal 21:204–229

    Article  MathSciNet  Google Scholar 

  33. Coifman RR, Maggioni M (2006) Diffusion wavelets. Appl Comput Harmonic Anal 21:53–94

    Article  MathSciNet  Google Scholar 

  34. Grossmann A, Morlet J (1984) Decomposition of hardy functions into square integrable wavelets of constant shape. Soc Ind Appl Math J Math Anal 15:723–736

    MathSciNet  MATH  Google Scholar 

  35. Holschneider M (1996) Continuous wavelet transforms on the sphere. J Math Phys 37:4156–4165

    Article  MathSciNet  Google Scholar 

  36. Roşca D (2005) Locally supported rational spline wavelets on the sphere. Math Computa 74(252):1803–1829

    Article  MathSciNet  Google Scholar 

  37. Roşca D (2005) Haar wavelets on spherical triangulations”, advances in multiresolution for geometric modelling, part of the mathematics and visualization. Springer, Berlin, pp 407–419. https://doi.org/10.1007/3-540-26808-1_23

    Book  Google Scholar 

  38. Roşca D (2006) Piecewise constant wavelets defined on closed surfaces. J Comput Anal Appl 8(2):121–132

    MathSciNet  MATH  Google Scholar 

  39. Roşca D (2007) Weighted haar wavelets on the sphere. Int J Wavelets Multiresolut Inf Process 5(3):501–511

    Article  MathSciNet  Google Scholar 

  40. Roşca D (2007) Wavelet bases on the sphere obtained by radial projection. J Fourier Anal Appl 13(4):421–434

    Article  MathSciNet  Google Scholar 

  41. Wiaux Y, McEwen JD, Vandergheynst P, Blanc O (2008) Exact reconstruction with directional wavelets on the sphere. Monthly Notices R Astron Soc 388:770–788

    Article  Google Scholar 

  42. Wiaux Y, Jacques L, Vandergheynst P (2005) Correspondence principle between spherical and Euclidean wavelets. Astrophys J 632:15–28

    Article  Google Scholar 

  43. Debnath L, Shah FA (2017) Lecture notes on wavelet transforms, First edition, pp 1–220, ISBN-10: 9783319594323

  44. Mallat S (2008) A wavelet tour of signal processing: the sparse way, Academic Press; Third edition, pp 1–832, ISBN-10: 9780123743701

  45. Nason V (2015) Discrete wavelet transform, Clanrye International, Second edition, pp 1–232, ISBN-10: 1632401479

  46. Jensen A, Anders la cour-harbo (2001) Ripples in mathematics: the discrete wavelet transform, Springer, pp.1-246, ISBN-10: 3540416625

  47. Rao RM, Bopardikar AS (1998) Wavelet Transforms, Pearson Education, pp 1–496, ISBN-10: 8131705315

  48. Proakis JG, Manolakis DG (2007) Digital signal processing: principles, algorithms, and applications, Pearson Education India, Fourth edition, pp 1–1156, ISBN-10: 9788131710005

  49. Walnut DF (2008) An Introduction to Wavelet Analysis, Springer, pp 1–452, ISBN-10: 8184890206

  50. Pinsky MA (2012) Introduction to fourier analysis and wavelets”, Orient Blackswan Private Limited - New Delhi, pp 1–376, ISBN-10: 0821887122

  51. Salimath C (2011) Wavelets—a brief introduction to theory and applications, LAP Lambert Academic Publishing, pp 1–144, ISBN-10: 3843391823

  52. Koornwinder TH (1993) Wavelets: an elementary treatment of theory and applications (Series In Approximations And Decompositions), World Scientific Publishing, pp 1–240, ISBN-10: 9810224869

  53. Udhaya Kumar S, Inbarani HH (2015) A novel neighborhood rough set based classification approach for medical diagnosis. Procedia Comput Sci 47:351–359

    Article  Google Scholar 

  54. Sang Y, Liang J, Qian Y (2016) Decision-theoretic rough sets under dynamic granulation. Knowl Based Syst 91:84–92

    Article  Google Scholar 

  55. Liu D, Li T, Liang D (2013) Three-Way Decisions in Dynamic Decision-Theoretic Rough Sets”, Rough Sets and Knowledge Technology 2013, Lecture Notes in Artificial Intelligence, Vol. 8171, pp 291–301

  56. Qian Y, Zhang H, Sang Y, Liang J (2014) Multigranulation decision-theoretic rough sets. Int J Approx Reason 55(1):225–237

    Article  MathSciNet  Google Scholar 

  57. Jothi G, Inbarani HH, Azar AT, Devi KR (2018) Rough set theory with jaya optimization for acute lymphoblastic leukemia classification. Neural Comput Appl 1–20

  58. Azam N, Yao J (2014) Game-theoretic rough sets for recommender systems. Knowl Based Syst 72:96–107

    Article  Google Scholar 

Download references

Acknowledgement

The authors are thankful to the Reviewers for their valuable suggestion to improve the paper. This Paper has not been published in whole or in part elsewhere. The manuscript is not currently being considered for publication in another journal. All authors have been personally and actively involved in substantive work leading to the manuscript, and will hold themselves jointly and individually responsible for its content. Both authors has no conflicts of interest to declare. This article does not contain any studies with human participants performed by any of the authors.

Author information

Authors and Affiliations

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

    K. Renuga Devi & H. Hannah Inbarani

Authors
  1. K. Renuga Devi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. Hannah Inbarani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Renuga Devi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Renuga Devi, K., Hannah Inbarani, H. Neighborhood based decision theoretic rough set under dynamic granulation for BCI motor imagery classification. J Multimodal User Interfaces 15, 301–321 (2021). https://doi.org/10.1007/s12193-020-00358-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12193-020-00358-4

Keywords

Search

Navigation