Skip to main content
SpringerLink
Log in

A Comparative Analysis of Different Feature Extraction Techniques for Motor Imagery Based BCI System

  • Conference paper
  • First Online:
Book cover Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

Brain Computer Interface (BCI) system based on Motor Imagery (MI) signals is one among the most prospering systems in the field of BCI. Motor imagination does not involve motor output from the human. This can be used to help motor disabled people to accomplish elementary tasks by themselves. One of the primary difficulties facing MI-based BCIs is extracting discriminative features from the EEG signal. This study seeks to minimize the number of features input to classifier by finding out the best feature extraction techniques that can achieve high system accuracy with minimum computation cost. To achieve this purpose, this study compares five different feature extraction techniques; Root Mean Square (RMS), Renyi entropy, Shannon entropy, Katz fractal dimension and Common Spatial Patterns (CSP), regarding accuracy and execution time. To ensure that these techniques are reliable; they are applied on three benchmark datasets; BCI Competition III datasets IVa and IIIa, and BCI Competition IV dataset IIa. The extracted features are examined with two classifiers; Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Experimental results showed that CSP is the best feature extraction technique compared to other examined techniques, yet Renyi entropy has the least computation time which is a critical issue for an online system.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kim, Y.K., Park, E., Lee, A., Im, C.H., Kim, Y.-H.: Changes in network connectivity during motor imagery and execution. PLoS One 13, 1–18 (2018)

    Google Scholar 

  2. Kumar, S.U., Inbarani, H.H.: PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task. Neural Comput. Appl. 28, 3239–3258 (2016)

    Article  Google Scholar 

  3. Ofner, P., Schwarz, A., Pereira, J., Wyss, D., Wildburger, R., Müller-Putz, G.R.: Attempted arm and hand movements can be decoded from low-frequency EEG from persons with spinal cord injury. Sci. Rep. 9, 7134 (2019)

    Article  Google Scholar 

  4. Hamedi, M., Salleh, S.-H., Noor, A.M., Mohammad-Rezazadeh, I.: Neural network-based three-class motor imagery classification using time-domain features for BCI applications. In: Presented at the 2014 IEEE Region 10 Symposium (2014)

    Google Scholar 

  5. Selim, S., Tantawi, M., Shedeed, H., Badr, A.: Reducing execution time for real-time motor imagery based BCI systems. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds.) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016, AISI 2016. Advances in Intelligent Systems and Computing, vol. 533. Springer, Cham (2017)

    Google Scholar 

  6. 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)

    Article  Google Scholar 

  7. Lindig-León, C., Bougrain, L.: A multi-label classification method for detection of combined motor imageries. In: IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China, pp. 3128–3133 (2015)

    Google Scholar 

  8. Ramoser, H., Muller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng. 8, 441–446 (2000)

    Article  Google Scholar 

  9. Selim, S., Tantawi, M., Shedeed, H., Badr, A.: A CSP\AM-BA-SVM approach for motor imagery BCI system. IEEE Access 6, 49192–49208 (2018)

    Article  Google Scholar 

  10. Kee, C.-Y., Ponnambalam, S.G., Loo, C.-K.: Binary and multi-class motor imagery using Renyi entropy for feature extraction. Neural Comput. Appl. 28, 2051–2062 (2017)

    Article  Google Scholar 

  11. Selim, S., Tantawi, M., Shedeed, H., Badr, A.: Comparing multi-class approaches for motor imagery using Renyi entropy. In: Hassanien, A., Tolba, M., Shaalan, K., Azar, A. (eds.) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018, AISI 2018. Advances in Intelligent Systems and Computing, vol. 845. Springer, Cham (2019)

    Google Scholar 

  12. Loo, C.K., Samraj, A., Lee, G.C.: Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface. Discrete Dyn. Nat. Soc. 2011, 8 (2011)

    Article  MathSciNet  Google Scholar 

  13. Krishna, D.H., Pasha, I.A., Savithri, T.S.: Classification of EEG motor imagery multi class signals based on cross correlation. Procedia Comput. Sci. 85, 490–495 (2016)

    Article  Google Scholar 

  14. Saa, J.F.D., Çetin, M.: Discriminative methods for classification of asynchronous imaginary motor tasks from EEG data. IEEE Trans. Neural Syst. Rehabil. Eng. 21, 1–8 (2012)

    Google Scholar 

  15. Mirnaziri, M., Rahimi, M., Alavikakhaki, S., Ebrahimpour, R.: Using Combination of μ, β and γ bands in classification of EEG signals. Neuroscience 4, 76–87 (2013)

    Google Scholar 

  16. Singla, R., Sharma, N.: Function classification of EEG signals based on ANN. Int. J. Soft Comput. Eng. 3, 158–163 (2014)

    Google Scholar 

  17. Bai, X., Wang, X., Zheng, S., Yu, M.: The offline feature extraction of four-class motor imagery EEG based on ICA and Wavelet-CSP. In: The 33rd Chinese Control Conference, Nanjing, China (2014)

    Google Scholar 

  18. Müller, K.-R., Blankertz, B.: BCI Competition III Dataset IVa. http://www.bbci.de/competition/iii/

  19. Wang, S., James, C.J.: Extracting rhythmic brain activity for braincomputer interfacing through constrained independent component analysis. Comput. Intell. Neurosci. 2007, 1–9 (2007)

    Article  Google Scholar 

  20. Pfurtscheller, G., Schlögl, A.: BCI Competition III Dataset IIIa. http://www.bbci.de/competition/iii/

  21. Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., et al.: Review of the BCI competition IV. Front. Neurosci. 6, 55 (2012)

    Article  Google Scholar 

  22. Rényi, A.: On a new axiomatic theory of probability. Acta Mathematica Academiae Scientiarum Hungaricae 6, 285–335 (1955)

    Article  MathSciNet  MATH  Google Scholar 

  23. Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.K.: Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed. 80, 187–194 (2005)

    Article  Google Scholar 

  24. Katz, M.J.: Fractals and the analysis of waveforms. Comput. Biol. Med. 18, 145–156 (1988)

    Article  Google Scholar 

  25. Bashashati, A., Fatourechi, M., Ward, R.K., Birch, G.E.: A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. J. Neural Eng. 4, R32–R57 (2007)

    Article  Google Scholar 

  26. Balakrishnama, S., Ganapathiraju, A.: Linear Discriminant Analysis - A brief tutorial. Institute for Signal and Information Processing (1998)

    Google Scholar 

  27. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Wiley, New York (1995)

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Media Engineering and Technology, German University in Cairo, Cairo, Egypt

    Sahar Selim

  2. Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt

    Manal Tantawi & Howida Shedeed

  3. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Amr Badr

Authors
  1. Sahar Selim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manal Tantawi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Howida Shedeed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amr Badr
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sahar Selim .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Selim, S., Tantawi, M., Shedeed, H., Badr, A. (2020). A Comparative Analysis of Different Feature Extraction Techniques for Motor Imagery Based BCI System. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_69

Download citation

Publish with us

Policies and ethics

Search

Navigation