Skip to main content

Advertisement

SpringerLink
Log in

Classification of motor imagery EEG signals using SVM, k-NN and ANN

  • Special Issue REDSET 2016 of CSIT
  • Published:
CSI Transactions on ICT Aims and scope Submit manuscript

Abstract

Presently, the brain mechanisms are still far from being fully understood, and a considerable amount of neuroscience research is still required to achieve this goal. Brain signals once decoded can be used to control devices and help people in locked-in state to live a better life. The present investigation deals with the processing and classification of left hand motor imagery and foot motor imagery EEG based brain signals. These signals have a very high dimensionality which possess problem for classifiers. In the present investigation, dimensionality reduction methods PCA and LDA have been implemented and state vector machine, k-nearest neighbour and artificial neural network (ANN) classifiers have been compared for their accuracy and speed of classification. It has been concluded that the combination of LDA and ANN can be treated as a strong candidate for processing and classification of Motor Imagery EEG based brain signals.

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

Similar content being viewed by others

References

  1. Huang D, Lin P, Fei D, Chen X, Bai O (1990) EEG-based online two dimensional cursor control. In: Proceedings of IEEE EBMS, pp 4547–4550, 3–6 Sept 1990

  2. Mak JN, Wolpaw JR (2009) Clinical applications of brain computer interfaces: current state and future prospects. IEEE Rev Biomed Eng 2:187–199

    Article  Google Scholar 

  3. Hjelm SI, Browall C (2006) Brainball—using brain activity for cool competition. In: Proceedings of 1st Nordic conference on human computer interaction, pp 59–60

  4. Krepki R, Blankertz B, Curio G, Muller KR (2007) The Berlin brain computer interface (BBCI)—towards a new communication channel for online control in gaming applications. Multimed Tools Appl 33(1):73–90

    Article  Google Scholar 

  5. Oude Bos DP, Reuderink B, van de Laar B, Gurkok H, Muhl C, Mannes P, Nijholt A, Heylen D (2010) Brain computer interfacing and games. In: Tan DS, Nijholt A (eds) Brain computer interfaces, human computer interaction series. Springer, London, pp 149–178

  6. Tyagi A, Nehra V (2013) Brain–computer interface: a thought translation device turning fantasy into reality. Int J Biomed Eng Technol 1:197–211

    Article  Google Scholar 

  7. Birbaumer N (2006) Breaking the silence: brain computer interfaces (BCI) for communication and motor control. Psychophysiology 4:517–532

    Article  Google Scholar 

  8. Wang Ying, Zhang Zhiguang, Li Yong, Gao Xiaorong, Gao Shangkai, Yang Fusheng (2004) BCI competition 2003-data set iv: an algorithm based on CSSD and FDA for classifying single trial EEG. IEEE Trans Biomed Eng 51:1081–1086

    Article  Google Scholar 

  9. Blankertz B, Dornhege G, Krauledat M, Muller KR, Curio G (2007) The non-invasive Berlin brain–computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37:539–550

    Article  Google Scholar 

  10. Sanei S, Chamber JA (2007) EEG signal processing. Wiley, England

    Book  Google Scholar 

  11. Smith L (2002) A tutorial on principal components analysis. Available at: www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf. Accessed 15 May 2015

  12. Duda RO, Hart PE, Stork DG (2006) Pattern classification, 2nd edn. John Wiley & Sons Inc., UK

    MATH  Google Scholar 

  13. Borisoff JF, Mason SG, Bashashati A, Birch GE (2004) Brain–computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch. IEEE Trans Biomed Eng 51:985–992

    Article  Google Scholar 

  14. Alpaydin E (2010) Introduction to machine learning, 2nd edn. PHI Learning Pvt. Limited, New Delhi

    MATH  Google Scholar 

  15. Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification (Technical report), Department of Computer Science and Information Engineering, National Taiwan University, pp 1–16

  16. Bailey T, Jain A (1978) A note on distance-weighted k-nearest neighbour rules. IEEE Trans Syst Man Cybern 8:311–313

    Article  MATH  Google Scholar 

  17. Sridhar GV, Mallikarjuna Rao P (2012) A neural network approach for EEG classification in BCI. Int J Comput Sci Telecommun 3:44–48

    Google Scholar 

  18. Ma Y, Ding X, She Q, Luo Z, Potter T, Zhang Y (2016) Classification of motor imagery EEG signals with support vector machines and particle swarm optimization. Comput Math Methods Med 2006(4941235):8

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Bhagat Phool Singh Mahila Vishwavidyalaya, Sonepat, Haryana, India

    Aruna Tyagi & Vijay Nehra

Authors
  1. Aruna Tyagi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vijay Nehra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aruna Tyagi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tyagi, A., Nehra, V. Classification of motor imagery EEG signals using SVM, k-NN and ANN. CSIT 4, 135–139 (2016). https://doi.org/10.1007/s40012-016-0091-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40012-016-0091-2

Keywords

Search

Navigation