Abstract
Spatial filtering is important for EEG signal processing since raw scalp EEG potentials have a poor spatial resolution due to the volume conduction effect. Extreme energy ratio (EER) is a recently proposed feature extractor which exhibits good performance. However, the performance of EER will be degraded by some factors such as outliers and the time-variances between the training and test sessions. Unfortunately, these limitations are common in the practical brain-computer interface (BCI) applications. This paper proposes a new feature extraction method called importance-weighted EER (IWEER) by defining two kinds of weight termed intra-trial importance and inter-trial importance. These weights are defined with the density ratio theory and assigned to the data points and trials respectively to improve the estimation of covariance matrices. The spatial filters learned by the IWEER are both robust to the outliers and adaptive to the test samples. Compared to the previous EER, experimental results on nine subjects demonstrate the better classification ability of the IWEER method.
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
Sun, S.: The Extreme Energy Ratio Criterion for EEG Feature Extraction. In: Kůrková, V., Neruda, R., KoutnÃk, J. (eds.) ICANN 2008, Part II. LNCS, vol. 5164, pp. 919–928. Springer, Heidelberg (2008)
Hill, N.J., Lal, T.N., Schröder, M., Hinterberger, T., Widman, G., Elger, C.E., Schölkopf, B., Birbaumer, N.: Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signal. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 404–413. Springer, Heidelberg (2006)
Müller, K.-R., Anderson, C.W., Birch, G.E.: Linear and non-linear Methods for Brain-Computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11(2), 165–169 (2003)
Krauledat, M.: Analysis of Nonstationarities in EEG Signals for Improving Brain-Computer interface Performance. PhD thesis, Technische Universitfit Berlin, Fakultfit IV –Elektrotechnik und Informatik (2008)
Sugiyama, M., Kanamori, T., Suzuki, T., Hido, S., Sese, J., Takeuchi, I., Wang, L.: A Density-Ratio Framework for Statistical Data Processing. Information and Media Technologies 4(4), 962–987 (2009)
Sugiyama, M., Suzuki, T., Nakajima, S., Kashima, H., von Bünau, P., Kawanabe, M.: Direct importance Estimation for Covariate Shift Adaptation. Annals of the Institute of Statistical Mathematics 60(4), 699–746 (2008)
Sajda, P., Gerson, A., Mller, K.R., Blankertz, B., Parra, L.: A Data Analysis Competition to Evaluate Machine Learning Algorithms for Use in Brain-Computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11(2), 184–185 (2003)
Nunez, P.L., Srinivasan, R., Westdorp, A.F., Wijesinghe, D.M., Tucker, R.B., Cadusch, P.J.: EEG coherency I: Statistics, Reference Electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scale. Electroenceph. Clinical Neurophysiology 103, 499–515 (1997)
Müller-Gerking, J., Pfurtscheller, G., Flyvbjerg, H.: Designing Optimal Spatial Filters for Single-trial EEG Classification in a Movement Task. Clinical Neurophysiology 110(5), 787–798 (1999)
Millán, J.R.: On the Need for On-Line Learning in Brain-Computer interfaces. In: IEEE International Conference on Neural Networks - Conference Proceedings, vol. 4, pp. 2877–2882 (2004)
Schalk, G., Blankertz, B., Chiappa, S., et al.: BCI competition III (2004–2005), http://ida.first.fraunhofer.de/projects/bci/competitioniii/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tu, W., Sun, S. (2010). Importance Weighted Extreme Energy Ratio for EEG Classification. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-17534-3_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17533-6
Online ISBN: 978-3-642-17534-3
eBook Packages: Computer ScienceComputer Science (R0)