Skip to main content

Advertisement

SpringerLink
Log in

A comparison approach toward finding the best feature and classifier in cue-based BCI

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

In this paper, a comparative evaluation of state-of-the art feature extraction and classification methods is presented for five subjects in order to increase the performance of a cue-based Brain–Computer interface (BCI) system for imagery tasks (left and right hand movements). To select an informative feature with a reliable classifier features containing standard bandpower, AAR coefficients, and fractal dimension along with support vector machine (SVM), Adaboost and Fisher linear discriminant analysis (FLDA) classifiers have been assessed. In the single feature-classifier combinations, bandpower with FLDA gave the best results for three subjects, and fractal dimension and FLDA and SVM classifiers lead to the best results for two other subjects. A genetic algorithm has been used to find the best combination of the features with the aforementioned classifiers and led to dramatic reduction of the classification error and also best results in the four subjects. Genetic feature combination results have been compared with the simple feature combination to show the performance of the Genetic algorithm.

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

Similar content being viewed by others

References

  1. Andrew Webb (1999) Statistical pattern recognition, Oxford University Press, New York

    MATH  Google Scholar 

  2. Bozorgzadeh Z, Birch GE, Mason SG (2000) The LF-ASD brain computer interface: on-line identification of imagined finger flexions in the spontaneous EEG of able-bodied subjects. IEEE Int Conf Acoustic Speech Proc 6:2385 –2388

    Google Scholar 

  3. Deriche M, Al-Ani A (2001) A new algorithm for EEG feature selection using mutual information. IEEE international conference on acoustics, speech, and signal processing 2001. Proceedings. ICASSP 2:1057–1060

  4. Duda RO, Hart PE, Stok DG (2001) Pattern classification. Wiley, New York

    MATH  Google Scholar 

  5. Esteller R, Vachtsevanos G, Echauz J, Lilt B (1999) A comparison of fractal dimension algorithms using synthetic and experimental data. ISCAS Proceedings, IEEE international symposium on circuits and systems 3:199–202

    Google Scholar 

  6. Esteller R, Vachtsevanos G, Echauz J, Henry T, Pennell P, Epstein C, Bakay R, Bowen C, Litt B (1999) Fractal dimension characterizes seizure onset in epileptic patients. Proc IEEE Int Conf Acoust Speech Signal process 4:2343–2346

    Google Scholar 

  7. Esteller R (2000) Detection of seizure onset in epileptic patients from intracranial EEG signals. Ph. D. thesis, School of Electrical and Computer Engineering, Georgia Institute of Technology

  8. Flotzinger D, Pregenzer M, Pfurtscheller G (1994) Feature selection with distinction sensitive learning vector quantisation and genetic algorithms. IEEE Int Conf Comput Intell 6:3448–3451

    Google Scholar 

  9. Goldberg (1989) Genetic algorithm, Prentice-Hall, New York

  10. Goldberger AL, Amaral LA, Hausdorff JM, Ivanov P, Peng CK, Stanley HE (2002) Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA 99(Suppl 1):2466–2472

    Article  Google Scholar 

  11. Graimann B, Huggins JE, Levine SP, Pfurtscheller G (2004) Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis. IEEE Trans Biomed Eng 51(6):954–962

    Article  Google Scholar 

  12. Guger C, Edlinger G, Harkam W, Niedermayer I, Pfurtscheller G (2003) How many people are able to operate an EEG-based Brain–Computer interface (BCI)? IEEE Trans Neural Syst Rehab Eng 11(2):145–147

    Article  Google Scholar 

  13. Haselsteiner E, Pfurtscheller G (2000) Using time-dependent neural networks for EEG classification. IEEE Trans on Rehab Eng 8:457–463

    Article  Google Scholar 

  14. Kalcher J, Flotzinger D, Pfurtscheller G (1992) A new approach to a Brain–Computer-interface (BCI) based on learning vector quantization (LVQ3). Proc Annu Int Conf IEEE 4:1658–1659

    Article  Google Scholar 

  15. Murphey YL, Zhihang Chen, Hong Guo (2001) Neural learning using AdaBoost. Proceedings IJCNN, International joint conference on neural networks 2:1037–1042

  16. Murua A (2002) Upper bounds for error rates of linear combination of classifiers. IEEE Trans Pattern Anal Mach intell 24(5):591–602

    Article  Google Scholar 

  17. Obermaier B, Neuper C, Guger C, Pfurtscheller G (2001) Information transfer rate in a five-classes Brain–Computer interface. IEEE Rehab Eng 9:283 –288

    Article  Google Scholar 

  18. Pfurtscheller G, Neuper C, Schlogl A, Lugger K (1998) Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans on Rehab Eng 6:316–328

    Article  Google Scholar 

  19. Pfurtscheller G;Lopes da Silva (1999) Event related desynchronization. Hand book of electroenceph. and clinical Neurophisiology. vol 6 revised edition. Elsevier, Amsterdam

  20. Pfurtscheller G, Neuper C (2001) Motor imagery and direct Brain–Computer communication. Proceedings of the IEEE 89:1123–1134

  21. Schlögl A, Flotzinger D, Pfurtscheller G (1997) Adaptive autoregressive modeling used for single-trial EEG classification. Biomed Tech 42:162–167

    Article  Google Scholar 

  22. Vapnic VN (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  23. Wolpaw JR, McFarland DJ, Vaughan TM (2000) Brain–Computer interface research at the Wadsworth Center. IEEE Trans Rehab Eng 8:222–226

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran

    R. Boostani

  2. Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    M. H. Moradi

  3. Laboratory of Brain–Computer Interfaces, Institute for Knowledge Discovery at the Graz, University of Technology, Graz, Austria

    B. Graimann & G. Pfurtscheller

Authors
  1. R. Boostani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Graimann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. H. Moradi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Pfurtscheller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Boostani.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Boostani, R., Graimann, B., Moradi, M.H. et al. A comparison approach toward finding the best feature and classifier in cue-based BCI. Med Bio Eng Comput 45, 403–412 (2007). https://doi.org/10.1007/s11517-007-0169-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-007-0169-y

Keywords

Search

Navigation