Abstract
This chapter introduces a modified version of the CC-LR presented in Chap. 8. The CC-LR algorithm was proposed for the identification of MI signals where the ‘Fp1’ electrode signal was randomly considered as the reference signal in the CC technique.
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
References
Blankertz, B., Muller, K..R., Krusienki, D. J., Schalk, G., Wolpaw, J.R., Schlogl, A., Pfurtscheller, S., Millan, J. De. R., Shrooder, M. and Birbamer, N. (2006) ‘The BCI competition III: validating alternative approaches to actual BCI problems’, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 14, no. 2, pp. 153–159.
De Veaux, R. D., Velleman, P.F. and Bock, D.E. (2008) Intro Stats (3rd edition), Pearson Addison Wesley, Boston.
Islam, M. N. (2004) An introduction to statistics and probability, 3rd ed., Mullick & brothers, Dhaka New Market, Dhaka-1205, pp. 160–161.
Lotte F and Guan C (2011) ‘Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms’ IEEE Transactions on Biomedical Engineering, Vol. 58, pp. 355–362.
Lu, H., Eng, H. L., Guan, C., Plataniotis, K. N. and Venetsanopoulos, A. N. (2010) ‘Regularized common spatial patterns with aggregation for EEG classification in small-sample setting’, IEEE Transactions on Biomedical Engineering, Vol. 57, no. 12 pp. 2936–2945.
Sander T H, Leistner S, Wabnitz H, Mackert B M, Macdonald R and Trahms L (2010) ‘Cross-correlation of motor activity signals from dc-magnetoencephalography, near-infrared spectroscopy and electromyography’, Computational Intelligence and Neuroscience, doi:10.1155/2010/78527.
Siuly, Li, Y. and Wen, P. (2011) ‘Clustering technique-based least square support vector machine for EEG signal classification’, Computer Methods and Programs in Biomedicine, Vol. 104, Issue 3, pp. 358–372.
Siuly, Y. Li, and P. Wen, (2014) ‘Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain computer interface’, Computer Methods and programs in Biomedicine, Vol. 113, no. 3, pp. 767–780.
Siuly, X. Yin, S. Hadjiloucas, Y. Zhang, (2016) ‘Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear classifiers’, Computer Methods and Programs in Biomedicine,127, 64–82.
Siuly, Y. Li, and P. Wen, (2013) ‘Identification of Motor Imagery Tasks through CC-LR Algorithm in Brain Computer Interface’, International Journal of Bioinformatics Research and Applications, Vol.9, no. 2, pp. 156–172.
Wu, W., Gao, X., Hong, B. and Gao, S. (2008) ‘Classifying single-trial EEG during motor imagery by iterative spatio-spectral patterns learning (ISSPL)’, IEEE Transactions on Biomedical Engineering, Vol. 55, no. 6, pp. 1733–1743.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this chapter
Cite this chapter
Siuly, S., Li, Y., Zhang, Y. (2016). Modified CC-LR Algorithm for Identification of MI-Based EEG Signals. In: EEG Signal Analysis and Classification. Health Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-47653-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-47653-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47652-0
Online ISBN: 978-3-319-47653-7
eBook Packages: Computer ScienceComputer Science (R0)