Abstract
This paper proposes a robust algorithm for adaptive modelling of EEG signal classification using a modified Extended Kalman Filter (EKF). This modified EKF combines Radial Basis functions (RBF) and Autoregressive (AR) modeling and obtains better classification performance by truncating the filtering distribution when new observations are very informative.
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© 2008 Springer-Verlag Berlin Heidelberg
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Yoon, J.W., Roberts, S.J., Dyson, M., Gan, J.Q. (2008). Adaptive Classification by Hybrid EKF with Truncated Filtering: Brain Computer Interfacing. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_47
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DOI: https://doi.org/10.1007/978-3-540-88906-9_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88905-2
Online ISBN: 978-3-540-88906-9
eBook Packages: Computer ScienceComputer Science (R0)