Skip to main content
SpringerLink
Log in

Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset

  • Original Article
  • Published:
Biomedical Engineering Letters Aims and scope Submit manuscript

Abstract

Steady-state visual evoked potential (SSVEP)-based brain-computer Interface (BCI) has demonstrated the potential to manage multi-command targets to achieve high-speed communication. Recent studies on multi-class SSVEP-based BCI have focused on synchronous systems, which rely on predefined time and task indicators; thus, these systems that use passive approaches may be less suitable for practical applications. Asynchronous systems recognize the user’s intention (whether or not the user is willing to use systems) from brain activity; then, after recognizing the user’s willingness, they begin to operate by switching swiftly for real-time control. Consequently, various methodologies have been proposed to capture the user’s intention. However, in-depth investigation of recognition methods in asynchronous BCI system is lacking. Thus, in this work, three recognition methods (power spectral density analysis, canonical correlation analysis (CCA), and support vector machine (SVM)) used widely in asynchronous SSVEP BCI systems were explored to compare their performance. Further, we categorized asynchronous systems into two approaches (1-stage and 2-stage) based upon the recognition process’s design, and compared their performance. To do so, a 40-class SSVEP dataset collected from 40 subjects was introduced. Finally, we found that the CCA-based method in the 2-stage approach demonstrated statistically significantly higher performance with a sensitivity of 97.62 ± 02.06%, specificity of 76.50 ± 23.50%, and accuracy of 75.59 ± 10.09%. Thus, it is expected that the 2-stage approach together with CCA-based recognition and FB-CCA classification have good potential to be implemented in practical asynchronous SSVEP BCI systems.

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. McFarland DJ, Wolpaw JR. Brain-computer interfaces for communication and control. Commun ACM. 2011;54(5):60–6.

    Article  Google Scholar 

  2. Lebedev MA, Nicolelis MA. Brain-machine interfaces: past, present and future. Trends Neurosci. 2006;29(9):536–46.

    Article  Google Scholar 

  3. Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM, et al. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng. 2000;8(2):164–73.

    Article  Google Scholar 

  4. Moghimi S, Kushki A, Marie Guerguerian A, Chau T. A review of EEG-based brain-computer interfaces as access pathways for individuals with severe disabilities. Assist Technol. 2013;25(2):99–110.

    Article  Google Scholar 

  5. Gao S, Wang Y, Gao X, Hong B. Visual and auditory brain-computer interfaces. IEEE Trans Biomed Eng. 2014;61(5):1436–47.

    Article  Google Scholar 

  6. Kritzman L, Eidelman-Rothman M, Keil A, Freche D, Sheppes G, Levit-Binnun N. Steady-state visual evoked potentials differentiate between internally and externally directed attention. Neuroimage. 2022;254: 119133.

    Article  Google Scholar 

  7. Wang Y, Wang R, Gao X, Hong B, Gao S. A practical VEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng. 2006;14(2):234–40.

    Article  Google Scholar 

  8. Galloway N. Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Br J Ophthalmol. 1990;74(4):255.

    Article  Google Scholar 

  9. Xia B, Li X, Xie H, Yang W, Li J, He L. Asynchronous brain-computer interface based on steady-state visual-evoked potential. Cogn Comput. 2013;5:243–51.

    Article  Google Scholar 

  10. Zhang W, Sun F, Wu H, Tan C, Ma Y. Asynchronous brain-computer interface shared control of robotic grasping. Tsinghua Sci Technol. 2019;24(3):360–70.

    Article  Google Scholar 

  11. Edelman BJ, Meng J, Suma D, Zurn C, Nagarajan E, Baxter B, Cline CC, He B. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci Robot. 2019;4(31):4.

    Article  Google Scholar 

  12. Moore MM. Real-world applications for brain-computer interface technology. IEEE Trans Neural Syst Rehabil Eng. 2003;11(2):162–5.

    Article  Google Scholar 

  13. Townsend G, Graimann B, Pfurtscheller G. Continuous EEG classification during motor imagery-simulation of an asynchronous BCI. IEEE Trans Neural Syst Rehabil Eng. 2004;12(2):258–65.

    Article  Google Scholar 

  14. Mangalampalli A, Pudi V. Far-hd: a fast and efficient algorithm for mining fuzzy association rules in large high-dimensional datasets, In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2013. pp. 1–6.

  15. Cao Z, Lin C-T. Inherent fuzzy entropy for the improvement of EEG complexity evaluation. IEEE Trans Fuzzy Syst. 2017;26(2):1032–5.

    Article  Google Scholar 

  16. Cao Z, Ding W, Wang Y-K, Hussain FK, Al-Jumaily A, Lin C-T. Effects of repetitive SSVEPS on EEG complexity using multiscale inherent fuzzy entropy. Neurocomputing. 2020;389:198–206.

    Article  Google Scholar 

  17. Rezeika A, Benda M, Stawicki P, Gembler F, Saboor A, Volosyak I. Brain-computer interface spellers: a review. Brain Sci. 2018;8(4):57.

    Article  Google Scholar 

  18. Li Y, Pan J, Wang F, Yu Z. A hybrid BCI system combining p300 and SSVEP and its application to wheelchair control. IEEE Trans Biomed Eng. 2013;60(11):3156–66.

    Article  Google Scholar 

  19. Liu Y-H, Wang S-H, Hu M-R. A self-paced p300 healthcare brain-computer interface system with SSVEP-based switching control and kernel FDA+ SVM-based detector. Appl Sci. 2016;6(5):142.

    Article  Google Scholar 

  20. Panicker RC, Puthusserypady S, Sun Y. An asynchronous p300 BCI with SSVEP-based control state detection. IEEE Trans Biomed Eng. 2011;58(6):1781–8.

    Article  Google Scholar 

  21. Suefusa K, Tanaka T. Asynchronous brain-computer interfacing based on mixed-coded visual stimuli. IEEE Trans Biomed Eng. 2017;65(9):2119–29.

    Article  Google Scholar 

  22. Abu-Alqumsan M, Peer A. Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces. J Neural Eng. 2016;13(3): 036005.

    Article  Google Scholar 

  23. da Cruz JN, Wan F, Wong CM, Cao T. Adaptive time-window length based on online performance measurement in SSVEP-based BCIS. Neurocomputing. 2015;149:93–9.

    Article  Google Scholar 

  24. Xia B, Li X, Xie H, Yang W, Li J, He L. Asynchronous brain-computer interface based on steady-state visual-evoked potential. Cogn Comput. 2013;5:243–51.

    Article  Google Scholar 

  25. Zhang W, Zhou T, Zhao J, Ji B, Wu Z. Recognition of the idle state based on a novel IFB-OCN method for an asynchronous brain-computer interface. J Neurosci Methods. 2020;341: 108776.

    Article  Google Scholar 

  26. Zhang D, Huang B, Wu W, Li S. An idle-state detection algorithm for SSVEP-based brain-computer interfaces using a maximum evoked response spatial filter. Int J Neural Syst. 2015;25(07):1550030.

    Article  Google Scholar 

  27. Soni D, Malan N. S, Sharma S. CCA model with training approach to improve recognition rate of SSVEP in real time, In: Proceedings of the 2019 3rd International Conference on Artificial Intelligence and Virtual Reality, 2019. pp. 56–59.

  28. Meriño L. M, Nayak T, Hall G, Pack D. J, Huang Y. Detection of control or idle state with a likelihood ratio test in asynchronous ssvep-based brain-computer interface systems, In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2016. pp. 1568–1571.

  29. Du J, Ke Y, Liu P, Liu W, Kong L, Wang N, Xu M, An X, Ming D. A two-step idle-state detection method for SSVEP BCI, In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019. pp. 3095–3098.

  30. Li X, Deng Z. Research on the fractal feature extraction based SSVEP idle-state detection. Int J Comput Commun Eng. 2012;1(4):331.

    Article  Google Scholar 

  31. Zhang Z, Deng Z. A two-stage state recognition method for asynchronous SSVEP-based brain-computer interface system, Jiqiren-Robot, 35(1), 2013.

  32. Han X, Lin K, Gao S, Gao X. A novel system of SSVEP-based human-robot coordination. J Neural Eng. 2018;16(1): 016006.

    Article  Google Scholar 

  33. Kaczmarek P, Salomon P. Towards SSVEP-based, portable, responsive brain-computer interface, In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2015. pp. 1095–1098.

  34. Pan J, Li Y, Zhang R, Gu Z, Li F. Discrimination between control and idle states in asynchronous SSVEP-based brain switches: a pseudo-key-based approach. IEEE Trans Neural Syst Rehabil Eng. 2013;21(3):435–43.

    Article  Google Scholar 

  35. Ren R, Bin G, Gao X. Idle state detection in ssvep-based brain-computer interfaces, In: 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, IEEE, 2008. pp. 2012–2015.

  36. Li Y, Pan J, Long J, Yu T, Wang F, Yu Z, Wu W. Multimodal BCIS: target detection, multidimensional control, and awareness evaluation in patients with disorder of consciousness. Proc IEEE. 2015;104(2):332–52.

    Google Scholar 

  37. Zhang L, Wu X, Guo X, Liu J, Zhou B. Design and implementation of an asynchronous BCI system with alpha rhythm and SSVEP. IEEE Access. 2019;7:146123–43.

    Article  Google Scholar 

  38. Zhou Y, He S, Huang Q, Li Y. A hybrid asynchronous brain-computer interface combining SSVEP and EOG signals. IEEE Trans Biomed Eng. 2020;67(10):2881–92.

    Article  Google Scholar 

  39. Wang N, Qian T, Zhuo Q, Gao X. Discrimination between idle and work states in bci based on SSVEP, In: 2010 2nd International Conference on Advanced Computer Control, IEEE, vol. 4, 2010. pp. 355–358.

  40. Cheng M, Gao X, Gao S, Xu D. Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng. 2002;49(10):1181–6.

    Article  Google Scholar 

  41. Brainard DH, Vision S. The psychophysics toolbox. Spat Vis. 1997;10(4):433–6.

    Article  Google Scholar 

  42. Chen X, Wang Y, Nakanishi M, Gao X, Jung T-P, Gao S. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci. 2015;112(44):E6058–67.

    Article  Google Scholar 

  43. Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng. 2018;15(3): 031005.

    Article  Google Scholar 

  44. Zhang D, Huang B, Wu W, Li S. An idle-state detection algorithm for SSVEP-based brain-computer interfaces using a maximum evoked response spatial filter. Int J Neural Syst. 2015;25(07):1550030.

    Article  Google Scholar 

  45. Wu T.-F, Lin C.-J, Weng R. Probability estimates for multi-class classification by pairwise coupling, Advances in Neural Information Processing Systems, vol. 16, 2003.

  46. Chang C-C, Lin C-J. Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST). 2011;2(3):1–27.

    Article  Google Scholar 

  47. Zerafa R, Camilleri T, Falzon O, Camilleri KP. To train or not to train? a survey on training of feature extraction methods for SSVEP-based BCIS. J Neural Eng. 2018;15(5): 051001.

    Article  Google Scholar 

  48. Kim H, Won K, Ahn M, Jun S. C. Cognitive-switch detection for un-cued ssvep bci speller, In: 2023 11th International Winter Conference on Brain-Computer Interface (BCI), IEEE, 2023. pp. 1–5.

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) Grants (No.2017-0-00451; No. 2019-0-01842) funded by the Korea government.

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Bukgu, Gwangju, 61005, Korea

    Heegyu Kim & Sung Chan Jun

  2. Hybrid Team, Inria, Univ Rennes, IRISA, CNRS, F35000, Rennes, France

    Kyungho Won

  3. School of Computer Science and Electrical Engineering, Handong Global University, Bukgu, Pohang, 37554, Korea

    Minkyu Ahn

  4. School of Artificial Intelligence, Gwangju Institute of Science and Technology, Bukgu, Gwangju, 61005, Korea

    Sung Chan Jun

Authors
  1. Heegyu Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kyungho Won
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Minkyu Ahn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sung Chan Jun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sung Chan Jun.

Ethics declarations

Conflict of interest

The authors have not disclosed any competing interests.

Consent to participate

This experiment was approved by the Institutional Review Board at Gwangju Institute of Science and Technology (20211201-HR-64-02-04), and all subjects were informed about the experimental procedure and signed informed consent to participate.

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

Kim, H., Won, K., Ahn, M. et al. Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset. Biomed. Eng. Lett. 14, 617–630 (2024). https://doi.org/10.1007/s13534-024-00357-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13534-024-00357-4

Keywords

Search

Navigation