Abstract
This paper presents a novel method based on deterministic annealing to circumvent the problem of the sensitivity to atypical observations associated with the maximum likelihood (ML) estimator via conventional EM algorithm for mixture models. In order to learn the mixture models in a robust way, the parameters of mixture model are estimated by trimmed likelihood estimator (TLE), and the learning process is controlled by temperature based on the principle of maximum entropy. Moreover, we apply the proposed method to the single-trial electroencephalography (EEG) classification task. The motivation of this work is to eliminate the negative effects of artifacts in EEG data, which usually exist in real-life environments, and the experimental results demonstrate that the proposed method can successfully detect the outliers and therefore achieve more reliable result.
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., et al.: Brain–computer interface technology: a review of the first international meeting. IEEE Transactions on Rehabilitation Engineering 8(2), 164–173 (2000)
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interface for communication and control. Clinical Neurophysiology 133(6), 767–791 (2002)
Bashashati, A., Fatourechi, M., Ward, R.K., Birch, G.E.: A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. Journal of Neural Engineering 4(2), R32–R57 (2007)
McFarland, D.J., Anderson, C.W., Müller, K.-R., Schlogl, A., Krusienski, D.J.: BCI meeting 2005-workshop on BCI signal processing: feature extraction and translation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2), 135–138 (2006)
Lotte, F., Congedo, M., Lecuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering 4(2), R1–R13 (2007)
McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, New York (2000)
Millán, J.R., Renkens, F., Mouriño, J., Gerstner, W.: Brain-actuated interaction. Artificial Intelligence 159, 241–259 (2004)
Millán, J.R.: On the need for on-line learning in brain–computer interfaces. In: Proceedings of International Joint Conference on Neural Networks, Budapest, Hungary, pp. 2877–2882 (2004)
Buttfield, A., Millán, J.R.: Online classifier adaptation in brain-computer interfaces. Techical Report, IDIAP–RR 06-16 (2006)
Sun, S., Zhang, C., Lu, N.: On the On-line Learning Algorithms for EEG Signal Classification in Brain Computer Interfaces. In: Wang, L., Jin, Y. (eds.) FSKD 2005. LNCS (LNAI), vol. 3614, pp. 638–647. Springer, Heidelberg (2005)
Sun, S., Zhang, C.: Learning On-line Classification via Decorrelated LMS Algorithm: Application to Brain–Computer Interfaces. In: Hoffmann, A., Motoda, H., Scheffer, T. (eds.) DS 2005. LNCS (LNAI), vol. 3735, pp. 215–226. Springer, Heidelberg (2005)
Schalk, G., Brunner, P., Gerhardt, L.A., Bischof, H., Wolpaw, J.R.: Brain-computer interfaces (BCIs): Detection instead of classification. Neuroscience Methods 167(1), 51–62 (2008)
Fazli, S., Danóczy, M., Popescu, F., Blankertz, B., Müller, K.-R.: Using Rest Class and Control Paradigms for Brain Computer Interfacing. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5517, pp. 651–665. Springer, Heidelberg (2009)
McLachlan, G., Krishnan, T.: The EM Algorithm and Extensions. Wiley, New York (1997)
Nguyen, D.T., Chen, L., Chan, C.K.: An outlier-aware data clustering algorithm in mixture model. In: Proceedings of 7th IEEE International Conference on Information, Communication and Signal Processing, Macau, China, pp. 1–5 (2009)
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Müller, K.-R.: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Processing Magazine 25(1), 41–56 (2008)
Hadi, A.S., Luceño, A.: Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms. Computational Statistics & Data Analysis 25(3), 251–272 (1997)
Neykov, N., Filzmoser, P., Dimova, R., Neytchev, P.: Robust fitting of mixtures using trimmed likelihood estimator. Computational Statistics & Data Analysis 52(1), 299–308 (2007)
Neykov, N., Müller, C.: Breakdown point and computation of trimmed likelihood estimators in generalized linear models. In: Developments in Robust Statistics, pp. 277–286. Physica-Verlag, Heidelberg (2003)
Rose, K., Gurewitz, E., Fox, G.C.: Statistical mechanics and phase transitions in clustering. Physical Review Letters 65(8), 945–948 (1990)
Rose, K.: Deterministic annealing for clustering, compression, classification, regression, and related optimization problems. Proceedings of the IEEE 86(11), 2210–2239 (1998)
Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming, version 1.21, http://cvxr.com/cvx
Rose, K., Gurewitz, E., Fox, G.C.: Constrained clustering as an optimization method. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(8), 785–794 (1993)
Machine Learning Repository website, http://archive.ics.uci.edu/ml/index.html
BCI competition IV website, http://bbci.de/competition/iv/
Ueda, N., Nakano, R.: Deterministic annealing EM algorithm. Neural Networks 11(2), 271–282 (1998)
Zhao, Q., Miller, D.J.: A deterministic, annealing-based approach for learning and model selection in finite mixture models. In: Proceedings of 29th IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, Canada, pp. V-457–V-460 (2004)
Figueiredo, M.A.T., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 381–396 (2002)
Zhang, B., Zhang, C., Yi, X.: Competitive EM algorithm for finite mixture models. Pattern Recognition 37(1), 131–144 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, B., Wan, F., Mak, P.U., Mak, P.I., Vai, M.I. (2012). Robust Learning of Mixture Models and Its Application on Trial Pruning for EEG Signal Analysis. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds) New Frontiers in Applied Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 7104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28320-8_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-28320-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28319-2
Online ISBN: 978-3-642-28320-8
eBook Packages: Computer ScienceComputer Science (R0)