Skip to main content

Advertisement

SpringerLink
Log in

Comparison of subject-independent and subject-specific EEG-based BCI using LDA and SVM classifiers

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

Abstract

Motor imagery brain–computer interface (MI-BCI) is one of the most used paradigms in EEG-based brain–computer interface (BCI). The current state-of-the-art in BCI involves tuning classifiers to subject-specific training data, acquired over several sessions, in order to perform calibration prior to actual use of the so-called subject-specific BCI system (SS-BCI). Herein, the goal is to provide a ready-to-use system requiring minimal effort for setup. Thus, our challenge was to design a subject-independent BCI (SI-BCI) to be used by any new user without the constraint of individual calibration. Outcomes from other studies with the same purpose were used to undertake comparisons and validate our findings. For the EEG signal processing, we used a combination of the delta (0.5–4 Hz), alpha (8–13 Hz), and beta+gamma (13–40 Hz) bands at a stage prior to feature extraction. Next, we extracted features from the 27-channel EEG using common spatial pattern (CSP) and performed binary classification (MI of right- and left-hand) with linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. These analyses were done for both the SS-BCI and SI-BCI models. We employed “leave-one-subject-out” (LOSO) arrangement and 10-fold cross-validation to evaluate our SI-BCI and SS-BCI systems, respectively. Compared with other two studies, our work was the only one that showed higher accuracy for the LDA classifier in SI-BCI as compared to SS-BCI. On the other hand, LDA accuracy was lower than accuracy achieved with SVM in both conditions (SI-BCI and SS-BCI). Our SS-BCI accuracy reached 76.85% using LDA and 94.20% using SVM and for SI-BCI we got 80.30% with LDA and 83.23% with SVM. We conclude that SI-BCI may be a feasible and relevant option, which can be used in scenarios where subjects are not able to submit themselves to long training sessions or to fast evaluation of the so called “BCI illiteracy.” Comparatively, our strategy proved to be more efficient, giving us the best result for SI-BCI when faced against the classification performances of other three studies, even considering the caveat that different datasets were used in the comparison of the four studies.

Graphical abstract

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

Similar content being viewed by others

References

  1. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113(6):767–791. https://doi.org/10.1016/S1388-2457(02)00057-3

    Article  PubMed  Google Scholar 

  2. Mcfarland DJ, Wolpaw JR (2005) Sensorimotor rhythm-based brain – computer regression improves performance. Neural Syst. Rehabil. Eng. IEEE Trans. 13(3):372–379. https://doi.org/10.1109/TNSRE.2005.848627

    Article  Google Scholar 

  3. Jackson PL, Lafleur MF, Malouin F, Richards C, Doyon J (2001) Potential role of mental practice using motor imagery in neurologic rehabilitation. Arch. Phys. Med. Rehabil. 82(8):1133–1141. https://doi.org/10.1053/apmr.2001.24286

    Article  CAS  PubMed  Google Scholar 

  4. C. Neuper and G. Pfurtscheller, “Neurofeedback training for BCI control.” Springer, pp. 65–78, 2010.

  5. B. Z. Allison and C. Neuper, “Could anyone use a BCI?” Springer Verlag, pp. 35–54, 2010.

  6. C. Jeunet, A. Cellard, S. Subramanian, M. Hachet, B. N’Kaoua, and F. Lotte, “How well can we learn with standard bci training approaches ? a pilot study . To cite this version : how well can we learn with standard bci training approaches ? a pilot study .,” 6th Int. Brain-Computer Interface Conf., no. Sepy, pp. 1–5, 2014, https://doi.org/10.3217/978-3-85125-378-8-83.

  7. F. Lotte, C. Guan, and K. K. Ang, “Comparison of designs towards a subject-independent brain-computer interface based on motor imagery,” Proc. 31st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. Eng. Futur. Biomed. EMBC 2009, pp. 4543–4546, 2009, https://doi.org/10.1109/IEMBS.2009.5334126.

  8. Fazli S, Popescu F, Danóczy M, Blankertz B, Müller KR, Grozea C (2009) Subject-independent mental state classification in single trials. Neural Networks 22(9):1305–1312. https://doi.org/10.1016/j.neunet.2009.06.003

    Article  PubMed  Google Scholar 

  9. Cantillo-Negrete J, Gutierrez-Martinez J, Carino-Escobar RI, Carrillo-Mora P, Elias-Vinas D (2014) An approach to improve the performance of subject-independent BCIs-based on motor imagery allocating subjects by gender. Biomed. Eng. Online 13(1):1–15. https://doi.org/10.1186/1475-925X-13-158

    Article  Google Scholar 

  10. Ray AM et al (2015) A subject-independent pattern-based brain-computer interface. Front. Behav. Neurosci. 9(OCTOBER):1–15. https://doi.org/10.3389/fnbeh.2015.00269

    Article  Google Scholar 

  11. Joadder MAM, Myszewski JJ, Rahman MH, Wang I (2019) A performance based feature selection technique for subject independent MI based BCI. Heal. Inf. Sci. Syst. 7(1):15. https://doi.org/10.1007/s13755-019-0076-2

    Article  Google Scholar 

  12. E. Jeon, W. Ko, J. S. Yoon, and H.-I. Suk, “Mutual information-driven subject-invariant and class-relevant deep representation learning in BCI,” Oct. 2019.

  13. F. Lotte and Cuntai Guan, “Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms,” IEEE Trans. Biomed. Eng 58 2 355–362 2011, https://doi.org/10.1109/TBME.2010.2082539.

  14. A. Meinel et al., “Tikhonov regularization enhances EEG-based spatial filtering for single trial regression To cite this version : HAL Id : hal-01655755,” 2017.

  15. F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” J. Neural Eng., vol. 4, no. 2, 2007, https://doi.org/10.1088/1741-2560/4/2/R01.

  16. Pfurtscheller G, Lopes Da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clin. Neurophysiol. 110(11):1842–1857. https://doi.org/10.1016/S1388-2457(99)00141-8

    Article  CAS  PubMed  Google Scholar 

  17. Cho H, Ahn M, Ahn S, Kwon M, Jun SC (2017) EEG datasets for motor imagery brain-computer interface. Gigascience 6(7):1–8. https://doi.org/10.1093/gigascience/gix034

    Article  PubMed  PubMed Central  Google Scholar 

  18. P. V. Ascencao, E. M. Santos, L. H. Lacerda, and F. J. Fraga, “Evaluation of performance metrics for users of brain computer interfaces during motor imagery,” 2019 IEEE Int. Conf. Syst. Man Cybern., pp. 217–222 2019, https://doi.org/10.1109/SMC.2019.8914393.

  19. Ramoser H, Müller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng. 8(4):441–446. https://doi.org/10.1109/86.895946

    Article  CAS  PubMed  Google Scholar 

  20. Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR (2008) Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process. Mag. 25(1):41–56. https://doi.org/10.1109/MSP.2008.4408441

    Article  Google Scholar 

  21. M.-G. J., P. G., and F. H., “Designing optimal spatial filters for single-trial EEG classification in a movement task,” Clin. Neurophysiol. 110 787–798, 1999.

  22. K. D. J. et al., “A comparison of classification techniques for the P300 speller.J Neural Eng 3 299–305, 2006, https://doi.org/10.1088/1741-2560/3/4/007.

  23. Cawley GC, Talbot NLC (2003) Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recognit. 36(11):2585–2592. https://doi.org/10.1016/S0031-3203(03)00136-5

    Article  Google Scholar 

  24. Mahmoudi A, Takerkart S, Regragui F, Boussaoud D, Brovelli A (2014) Multivoxel pattern analysis for fMRI data: a review. Comput. Math. Methods Med. 2012(June):2012. https://doi.org/10.1155/2012/961257

    Article  Google Scholar 

  25. A. E. Hassanien and A. T. Azar, “Intelligent systems reference library 74 Brain-computer interfaces current trends and applications,” 2015.

  26. E. M. dos Santos, R. Cassani, T. H. Falk, and F. J. Fraga, “Improved motor imagery brain-computer interface performance via adaptive modulation filtering and two-stage classification,” Biomed. Signal Process. Control, vol. 57. 101812 2020, https://doi.org/10.1016/j.bspc.2019.101812.

  27. Vidaurre C, Blankertz B (2010) Towards a cure for BCI illiteracy. Brain Topogr. 23(2):194–198. https://doi.org/10.1007/s10548-009-0121-6

    Article  PubMed  Google Scholar 

Download references

Funding

This work was conducted during first author’s PhD studies, with scholarship financed by the Brazilian Government through the National Council for the Improvement of Higher Education (CAPES). We would also like to thank the São Paulo Research Foundation (FAPESP) (grant numbers #2015/09510-7, #2017/15243-7) for supporting this study with equipment and software.

Author information

Authors and Affiliations

  1. UFABC, Federal University of the ABC, Santo André, Brazil

    Eliana M. dos Santos, Rodrigo San-Martin & Francisco J. Fraga

Authors
  1. Eliana M. dos Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rodrigo San-Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco J. Fraga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eliana M. dos Santos.

Ethics declarations

Ethical approval

This was not required.

Conflicts of interest

The authors declare no competing interests.

Additional information

Publisher's note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

dos Santos, E.M., San-Martin, R. & Fraga, F.J. Comparison of subject-independent and subject-specific EEG-based BCI using LDA and SVM classifiers. Med Biol Eng Comput 61, 835–845 (2023). https://doi.org/10.1007/s11517-023-02769-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-023-02769-3

Keywords

Search

Navigation