Skip to main content
SpringerLink
Log in

Improvement of Classification Accuracy in a Phase-Tagged Steady-State Visual Evoked Potential-Based Brain–Computer Interface Using Adaptive Neuron-Fuzzy Classifier

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

Steady-state visual evoked potential (SSVEP) has been used to design brain–computer interface (BCI) for a variety of applications, due to its advantages of high accuracy, fewer electrodes, and high information transfer rate. In recent years, researchers developed phase-tagged SSVEP-based BCI to overcome the problem of amplitude–frequency preference in traditional frequency-coded SSVEPs. However, the phase of SSVEP could be affected by subject’s attention and emotion, which sometimes causes ambiguity in discerning gazed targets when fixed phase margins were used for class classification. In this study, we adopted adaptive neuron-fuzzy classifier (ANFC) to improve the gaze-target detections. The SSVEP features in polar coordinates were first transformed into Cartesian coordinates, and then ANFC was utilized to improve the accuracy of gazed-target detections. The proposed ANFC-based approach has achieved 63.07 ± 8.13 bits/min.

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
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Mason, S.G., Birch, G.E.: A general framework for brain-computer interface design. IEEE Trans. Neural Syst. Rehabil Eng. 11(1), 70–85 (2003)

    Article  Google Scholar 

  2. Hinterberger, T., Weiskopf, N., Veit, R., Wilhelm, B., Betta, E., Birbaumer, N.: An EEG-driven brain-computer interface combined with functional magnetic resonance imaging (fMRI). IEEE Trans. Biomed. Eng. 51(6), 971–974 (2004)

    Article  Google Scholar 

  3. Georgopoulos, A.P., Langheim, F.J., Leuthold, A.C., Merkle, A.N.: Magnetoencephalographic signals predict movement trajectory in space. Exp. Brain Res. 167(1), 132–135 (2005)

    Article  Google Scholar 

  4. Curran, E.A., Stokes, M.J.: Learning to control brain activity: a review of the production and control of EEG components for driving brain–computer interface (BCI) systems. Brain Cogn. 51(3), 326–336 (2003)

    Article  Google Scholar 

  5. Weiskopf, N., Mathiak, K., Bock, S.W., Scharnowski, F., Veit, R., Grodd, W., Goebel, R., Birbaumer, N.: Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004)

    Article  Google Scholar 

  6. Chen, C.-H., Ho, M.-S., Shyu, K.-K., Hsu, K.-C., Wang, K.-W., Lee, P.-L.: A noninvasive brain computer interface using visually-induced near-infrared spectroscopy responses. Neurosci. Lett. 580, 22–26 (2014)

    Article  Google Scholar 

  7. Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., Flor, H.: A spelling device for the paralysed. Nature 398(6725), 297–298 (1999)

    Article  Google Scholar 

  8. Donchin, E., Spencer, K.M., Wijesinghe, R.: The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans. Rehabil. Eng. 8(2), 174–179 (2000)

    Article  Google Scholar 

  9. Lee, P.-L., Hsieh, J.-C., Wu, C.-H., Shyu, K.-K., Wu, Y.-T.: Brain computer interface using flash onset and offset visual evoked potentials. Clin. Neurophysiol. 119(3), 605–616 (2008)

    Article  Google Scholar 

  10. Huang, D., Qian, K., Fei, D.-Y., Jia, W., Chen, X., Bai, O.: Electroencephalography (EEG)-based brain–computer interface (BCI): A 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control. IEEE Trans. Neural Syst. Rehabil. Eng. 20(3), 379–388 (2012)

    Article  Google Scholar 

  11. Müller-Putz, G.R., Pfurtscheller, G.: Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Trans. Biomed. Eng. 55(1), 361–364 (2008)

    Article  Google Scholar 

  12. Ortner, R., Allison, B.Z., Korisek, G., Gaggl, H., Pfurtscheller, G.: An SSVEP BCI to control a hand orthosis for persons with tetraplegia. IEEE Trans. Neural Syst. Rehabil. Eng. 19(1), 1–5 (2011)

    Article  Google Scholar 

  13. Martinez, P., Bakardjian, H., Cichocki, A.: Fully online multicommand brain-computer interface with visual neurofeedback using SSVEP paradigm. Comput. Intell. Neurosci. 2007, 13 (2007)

    Article  Google Scholar 

  14. Hsu, H.-T., Lee, I.-H., Tsai, H.-T., Chang, H.-C., Shyu, K.-K., Hsu, C.-C., Chang, H.-H., Yeh, T.-K., Chang, C.-Y., Lee, P.-L.: Evaluate the feasibility of using frontal SSVEP to implement an SSVEP-based BCI in young, Elderly and ALS Groups. IEEE Trans. Neural Syst. Rehabil. Eng. 24(5), 603–615 (2016)

  15. Lin, Z., Zhang, C., Wu, W., Gao, X.: Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans. Biomed. Eng. 53(12), 2610–2614 (2006)

    Article  Google Scholar 

  16. Zhang, Y., Dong, L., Zhang, R., Yao, D., Zhang, Y., Xu, P.: An efficient frequency recognition method based on likelihood ratio test for SSVEP-based BCI. Comput. Math. Methods Med. (2014). doi:10.1155/2014/908719

  17. Zhang, Y., Jin, J., Qing, X., Wang, B., Wang, X.: LASSO based stimulus frequency recognition model for SSVEP BCIs. Biomed. Signal Process. Control 7(2), 104–111 (2012)

    Article  Google Scholar 

  18. Lee, P.-L., Sie, J.-J., Liu, Y.-J., Wu, C.-H., Lee, M.-H., Shu, C.-H., Li, P.-H., Sun, C.-W., Shyu, K.-K.: An SSVEP-actuated brain computer interface using phase-tagged flickering sequences: a cursor system. Ann. Biomed. Eng. 38(7), 2383–2397 (2010)

    Article  Google Scholar 

  19. Shyu, K.-K., Lee, P.-L., Lee, M.-H., Lin, M.-H., Lai, R.-J., Chiu, Y.-J.: Development of a low-cost FPGA-based SSVEP BCI multimedia control system. IEEE Trans.Biomed. Circ. Syst. 4(2), 125–132 (2010)

    Article  Google Scholar 

  20. Jia, C., Gao, X., Hong, B., Gao, S.: Frequency and phase mixed coding in SSVEP-based brain–computer interface. IEEE Trans. Biomed. Eng. 58(1), 200–206 (2011)

    Article  Google Scholar 

  21. Zhu, D., Molina, G. G., Mihajlović, V., Aarts, R. M.: Phase synchrony analysis for SSVEP-based BCIs. In: 2010 2nd International Conference on Computer Engineering and Technology (ICCET) IEEE (2010)

  22. Morgan, S., Hansen, J., Hillyard, S.: Selective attention to stimulus location modulates the steady-state visual evoked potential. Proc. Natl. Acad. Sci. 93(10), 4770–4774 (1996)

    Article  Google Scholar 

  23. Silberstein, R.B., Pipingas, A.: Steady-state visually evoked potential topography during the Wisconsin card sorting test. Electroencephalogr. Clin. Neurophysiol. 96(1), 24–35 (1995)

    Article  Google Scholar 

  24. Thompson, J.C., Tzambazis, K., Stough, C., Nagata, K., Silberstein, R.B.: The effects of nicotine on the 13 Hz steady-state visually evoked potential. Clin. Neurophysiol. 111(9), 1589–1595 (2000)

    Article  Google Scholar 

  25. Bakardjian, H., Tanaka, T., Cichocki, A.: Emotional faces boost up steady-state visual responsesforbrain–computer interface. NeuroReport 22(3), 121–125 (2011)

    Article  Google Scholar 

  26. Silberstein, R.B., Nunez, P.L., Pipingas, A., Harris, P., Danieli, F.: Steady state visually evoked potential (SSVEP) topography in a graded working memory task. Int. J. Psychophysiol. 42(2), 219–232 (2001)

    Article  Google Scholar 

  27. Gray, M., Kemp, A., Silberstein, R., Nathan, P.: Cortical neurophysiology of anticipatory anxiety: an investigation utilizing steady state probe topography (SSPT). Neuroimage 20(2), 975–986 (2003)

    Article  Google Scholar 

  28. Falzon, O., Camilleri, K., Muscat, J.: Complex-valued spatial filters for SSVEP-based BCIs with phase coding. IEEE Trans. Biomed. Eng. 59(9), 2486–2495 (2012)

    Article  Google Scholar 

  29. Kayikcioglu, T., Aydemir, O.: A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data. Pattern Recogn. Lett. 31(11), 1207–1215 (2010)

    Article  Google Scholar 

  30. Pfurtscheller, G., Kalcher, J., Neuper, C., Flotzinger, D., Pregenzer, M.: On-line EEG classification during externally-paced hand movements using a neural network-based classifier. Electroencephalogr. Clin. Neurophysiol. 99(5), 416–425 (1996)

    Article  Google Scholar 

  31. Lotte, F.: The use of fuzzy inference systems for classification in EEG-based brain-computer interfaces. In: 3rd International Brain–Computer Interfaces Workshop and Training Course (2006)

  32. Güler, I., Übeyli, E.D.: Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J. Neurosci. Methods 148(2), 113–121 (2005)

    Article  Google Scholar 

  33. Hsu, W.-Y.: Motor imagery electroencephalogram analysis using adaptive neural-fuzzy classification. Int. J. Fuzzy Syst. 16, 111–120 (2014)

    Google Scholar 

  34. Begum, D., Ravikumar, K., Mathew, J., Kubakaddi, S., Yadav, R.: EEG based patient monitoring system for mental alertness using adaptive Neuro-Fuzzy approach. J. Med. Bioeng. 4(1) (2015)

  35. Bhattacharyya, S., Basu, D., Konar, A., Tibarewala, D.: Interval type-2 fuzzy logic based multiclass ANFIS algorithm for real-time EEG based movement control of a robot arm. Robot. Auton. Syst. 68, 104–115 (2015)

    Article  Google Scholar 

  36. Misulis, K.E.: Spehlmann’s evoked potential primer: visual, auditory, and somatosensory evoked potentials in clinical diagnosis. Butterworth-Heinemann Medical, Oxford (1994)

    Google Scholar 

  37. Alpaydin, E.: Introduction to Machine Learning. MIT press, Cambridge (2014)

    MATH  Google Scholar 

  38. Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)

    Article  Google Scholar 

  39. Chang, H.-C., Lee, P.-L., Lo, M.-T., Lee, I., Yeh, T.-K., Chang, C.-Y.: Independence of amplitude-frequency and phase calibrations in an SSVEP-based BCI using stepping delay flickering sequences. IEEE Trans. Neural Syst. Rehabil. Eng. 20(3), 305–312 (2012)

    Article  Google Scholar 

  40. Kelly, S.P., Lalor, E.C., Reilly, R.B., Foxe, J.J.: Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. IEEE Trans. Neural Syst. Rehabil. Eng. 13(2), 172–178 (2005)

    Article  Google Scholar 

  41. Asadpour, V., Ravanfar, M. R., Fazel-Rezai, R.: Adaptive network fuzzy inference systems for classification in a brain computer interface. In: Fazel-Rezai, F. (ed.) Brain-Computer Interface Systems - Recent Progress and Future Prospects. Intex (2013). doi:10.5772/55989

  42. Wu, H.-Y., Lee, P.-L., Chang, H.-C., Hsieh, J.-C.: Accounting for phase drifts in SSVEP-based BCIs by means of biphasic stimulation. IEEE Trans. Biomed. Eng. 58(5), 1394–1402 (2011)

    Article  Google Scholar 

Download references

Acknowledgments

This study was funded by the National Central University, Ministry of Science and Technology (104-3115-E-008-001, 103-2511-S-008-003-MY2, 103- 2217-E-008-001, 103-2218-E-008-006, 102-2221-E- 008-086-MY3), Taipei Medical University Project (101TMUH-NCU-001, TMU102-AE1-B09).

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Nation Central University, No. 300, Zhongda Rd., Zhongli District, Taoyuan City, 32001, Taiwan, ROC

    Hao-Teng Hsu, Po-Lei Lee & Kuo-Kai Shyu

Authors
  1. Hao-Teng Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Po-Lei Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kuo-Kai Shyu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Po-Lei Lee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hsu, HT., Lee, PL. & Shyu, KK. Improvement of Classification Accuracy in a Phase-Tagged Steady-State Visual Evoked Potential-Based Brain–Computer Interface Using Adaptive Neuron-Fuzzy Classifier. Int. J. Fuzzy Syst. 19, 542–552 (2017). https://doi.org/10.1007/s40815-016-0248-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-016-0248-z

Keywords

Search

Navigation