Abstract
Among the EEG-based BCIs, SSVEP-based BCIs have gained much attention due to the advantages of relatively high information transfer rate (ITR) and short calibration time. Although in SSVEP-based BCIs the frequency recognition methods using multiple channels EEG signals may provide better accuracy, using single channel would be preferable in a practical scenario since it can make the system simple and easy-to-use. To this goal, we propose a new single channel method based on wavelet-independent component analysis (WICA) in the SSVEP-based BCI, in which wavelet transform (WT) is applied to decompose a single channel signal into several wavelet components and then independent component analysis (ICA) is applied to separate the independent sources from the wavelet components. Experimental results show that most of the time the recognition accuracy of the proposed single channel method is higher than the conventional single channel method, power spectrum (PS) method.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
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, 046002 (2009)
Guger, C., Allison, B.Z., Groβwindhager, B., Prückl, R., Hintermüller, C., Kapeller, C., Bruckner, M., Krausz, G., Edlinger, G.: How Many People Could Use an SSVEP BCI? Front Neurosci. 6 (2012)
Wu, Z., Yao, D.: Frequency Detection with Stability Coefficient for Steady-State Visual Evoked Potential (SSVEP)-Based BCIs. J. Neural Eng. 5, 36–43 (2008)
Wu, Z.: SSVEP Extraction Based on the Similarity of Background EEG. PloS One 9, e93884 (2014)
Lopez, M., Pelayo, A., Madrid, F., Prieto, E., Statistical Characterization, A.: of Steady-State Visual Evoked Potentials and Their Use in Brain–Computer Interfaces. Neural Processing Lett. 29, 179–187 (2009)
Nan, W., Wong, C.M., Wang, B., Wan, F., Mak, P.U., Mak, P.I., Vai, M.I.: A Comparison of Minimum Energy Combination and Canonical Correlation Analysis for SSVEP Detection. In: 5th International IEEE/EMBS Conference on Neural Engineering, pp. 469–472. IEEE Press, Cancun (2011)
Lin, J., Zhang, A.: Fault Feature Separation Using Wavelet-ICA Filter. NDT&E Int. 38, 421–427 (2005)
Comon, P.: Independent Component Analysis: a New Concept? Signal Processing 36, 287–314 (1994)
Nason, G.P., Silverman, B.W.: The Stationary Wavelet Transform and Some Statistical Applications. Wavelets and Statistics 103, 281–299 (1995)
Kirkove, M., Francois, C., Verly, J.: Comparative Evaluation of Existing and New Methods for Correcting Ocular Artifacts in Electroencephalographic Recordings. Signal Processing 98, 102–120 (2014)
Sheoran, M., Kumar, S., Kumar, A.: Wavelet-ICA Based Denoising of Electroencephalogram Signal. Int. J. Inform. Comput. Technol. 4, 1205–1210 (2014)
Hyvarinen, A., Karhunen, J., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Comput. 9, 1483–1492 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Yang, L., Wang, Z., Wong, C.M., Wan, F. (2015). Frequency Recognition Based on Wavelet-Independent Component Analysis for SSVEP-Based BCIs. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-25393-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25392-3
Online ISBN: 978-3-319-25393-0
eBook Packages: Computer ScienceComputer Science (R0)