Abstract
The steady-state visual evoked potential (SSVEP) has been widely applied in brain-computer interfaces (BCIs), such as letter or icon selection and device control. Most of these BCIs used different flickering frequencies to evoke SSVEP with different frequency components that were used as control commands. In this paper, a novel method combining the time phase and EEG frequency components is presented and validated with nine healthy subjects. In this method, four different frequency components of EEG were classified out from four time phases. When the SSVEP is evoked and what is the frequency of the SSVEP is determined by the linear discriminant analysis (LDA) classifier in the same time to locate the target image. The results from offline analysis show that this method yields good performance both in classification accuracy and information transfer rate (ITR).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002)
Zhu, D.H., Bieger, J., Molina, G.G., Aarts, R.M.: A survey of stimulation methods used in SSVEP-based BCIs. Comput. Intell. Neurosci. (2010), doi: 10.1155/2010/702357
Zhang, Y., Jin, J., Qing, X., Wang, B., Wang, X.: LASSO based stimulus frequency recognition model for SSVEP BCIs. Biomed. Signal Process. Control (2011), doi:10.1016/j.bspc.2011.02.002
Müller-Putz, G.R., Scherer, R., Brauneis, C., Pfurtscheller, G.: Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J. Neural Eng. 2, 123–130 (2005)
Friman, O., Lüth, T., Volosyak, I., Gräser, A.: Spelling with steady-state visual evoked potentials. In: Proceedings of the 3rd International IEEE/EMBS Conference on Neural Engineering (CNE 2007), Hawaii, May 2-5, pp. 510–523 (2007)
Gao, X., Xu, D., Cheng, M., Gao, S.: A BCI-based environmental controller for the motion-disabled. IEEE Trans. Neural Syst. Rehabi. Eng. 11, 137–140 (2003)
Graimann, B., Allison, B., Mandel, C., Lüth, T., Valbuena, D., Gräser, A.: Non-invasive brain-computer interfaces for semi-autonomous assistive devices. In: Schuster, A. (ed.) Robust Intelligent System, pp. 113–138. Springer, London (2008)
Li, T., Zhang, C., Ogihara, M.: A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20, 2429–2437 (2004)
Cheng, M., Gao, X., Gao, S., Xu, D.: Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans. Biomed. Eng. 49, 1181–1186 (2002)
Wang, Y., Wang, R., Gao, X., Hong, B., Gao, S.: A practical VEP-based brain-computer interface. IEEE Trans. Neural Syst. Rehab. Eng. 14, 234–239 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jin, J., Zhang, Y., Wang, X. (2011). A Novel Combination of Time Phase and EEG Frequency Components for SSVEP-Based BCI. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-24955-6_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24954-9
Online ISBN: 978-3-642-24955-6
eBook Packages: Computer ScienceComputer Science (R0)