Abstract
A crucial problem for the overall performance of steady-state visual evoked potentials (SSVEP)-based brain computer interface (BCIs) is the right choice of the time-window length since a large window results in a higher accuracy but longer detection time, making the system impractical. This paper proposes an adaptive time window length to improve the system performance based on the subject’s online performance. However, since there is no known methods of assessing the online performance in real time, it is also proposed a feedback from the user, through a speller, for the system to know whether the output is correct or not and change or maintain the time-window length accordantly. The system was implemented fully online and tested in 4 subjects. The subjects have attained an average information transfer rate (ITR) of 62.09bit/min and standard deviation of 2.13bit/min with a mean accuracy of 99.00% and standard deviation of 1.15%, which represents an improvement of about 6.50% of the ITR to the fixed time-window length system.
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References
Pfurtscheller, G., Neuper, C., Guger, C., Harkam, W., Ramoser, H., Schlogl, A., Obermaier, B., Pregenzer, M.: Current trends in Graz brain- computer interface (BCI) research. IEEE Trans. Rehabil. Eng. 8, 216–219 (2000)
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)
van Gerven, M., Farquhar, J., Schaefer, R., Vlek, R., Geuze, J., Nijholt, A., Ramsey, N., Haselager, P., Vuurpijl, L., Gielen, S., Desain, P.: The brain-computer interface cycle. J. Neural Eng. (6) (2009)
MacFarland, D.J., Krusienski, D.J., Wolpaw, J.R.: Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms. Prog. Brain Res. 411(9), 159 (2006)
Vialatte, F.-B., Maurice, M., Dauwels, J., Cichocki, A.: Steadystate visually evoked potentials: Focus on essential paradigms and future perspectives. Prog. Neurobiol. 90, 418–438 (2010)
Wang, Y., Gao, X., Hong, B., Jia, C., Gao, S.: Brain-Computer Interfaces Based on Visual Evoked Potentials: Feasibility of Practical System Designs. IEEE Eng. Med. Biol. Mag., 64–71 (2008)
Zhu, D., Bieger, J., Molina, G.G., Aarts, R.M.: A survey of stimulation methods used in SSVEP-based BCIs. Comput. Intell. Neurosci. (2010)
Bin, G., Gao, X., Yan, Z., Hong, B., Gao, S.: An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. J. Neural Eng. 6(4) (2009)
Beverina, F., Palmas, G., Silvoni, S., Piccione, F., Giove, S.: User adaptive BCIs: SSVEP and P300 based interfaces. PsychNol. J 1(4), 331–354 (2003)
Volosyak, I.: SSVEP based Bremen-BCI – boosting information transfer rates. J. Neural Eng. 8(3) (2011)
Valbuena, D., Volosyak, I., Gräser, A.: sBCI: Fast Detection of Steady-State Visual Evoked Potentials. In: Proc. Int. Conf. IEEE Eng. Med. Biol. Soc (EMBC 2010), pp. 3966–3969 (2010)
Millan, J.R., Mourino, J.: Asynchronous BCI and Local Neural Classifiers: An Overview of the Adaptive Brain Interface Project. IEEE Trans. Neural Syst. and Rehabil. Eng. 11(2) (2003)
Shenoy, P., Krauledat, M., Blankertz, B., Rao, R., Muller, K.R.: Towards adaptive classification for BCI. J. Neural Eng. 3, 13 (2006)
Girouard, A., Solovey, E.T., Hirshfield, L.M.: Adaptive Brain-Computer Interface. In: Brain Computer Interfaces, pp. 221–237. Springer (2010)
Wong, C.M.: Phase Information Enhanced Steady-State Visual Evoked Potential-based Brain-Computer Interface. Unpublished Masters Thesis. University of Macau (2011)
Cao, T., Wang, X.: Idle State Detection in SSVEP based BCI System: Algorithm, Implementation and Application, Unpublished Bachelor Thesis. University of Macau (2011)
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da Cruz, J.N., Wong, C.M., Wan, F. (2013). An SSVEP-Based BCI with Adaptive Time-Window Length. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_38
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DOI: https://doi.org/10.1007/978-3-642-39068-5_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39067-8
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