Abstract
Magneto- and electroencephalography (M/EEG) measure the electromagnetic signals produced by brain activity. In order to address the issue of limited signal-to-noise ratio (SNR) with raw data, acquisitions consist of multiple repetitions of the same experiment. An important challenge arising from such data is the variability of brain activations over the repetitions. It hinders statistical analysis such as prediction performance in a supervised learning setup. One such confounding variability is the time offset of the peak of the activation, which varies across repetitions. We propose to address this misalignment issue by explicitly modeling time shifts of different brain responses in a classification setup. To this end, we use the latent support vector machine (LSVM) formulation, where the latent shifts are inferred while learning the classifier parameters. The inferred shifts are further used to improve the SNR of the M/EEG data, and to infer the chronometry and the sequence of activations across the brain regions that are involved in the experimental task. Results are validated on a long term memory retrieval task, showing significant improvement using the proposed latent discriminative method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Adams, R., Fournier, J.: Sobolev Spaces: Pure and Applied Mathematics. Academic Press (2003)
Backus, A., Jensen, O., Meeuwissen, E., van Gerven, M., Dumoulin, S.: Investigating the temporal dynamics of long term memory representation retrieval using multivariate pattern analyses on magnetoencephalography data. MSc thesis (2011)
Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)
Bénar, C., Clerc, M., Papadopoulo, T.: Adaptive time-frequency models for single-trial M/EEG analysis. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 458–469. Springer, Heidelberg (2007)
Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine 34(4), 537–541 (1995)
Blankertz, B., Muller, K., Curio, G., Vaughan, T., Schalk, G., Wolpaw, J., Schlogl, A., Neuper, C., Pfurtscheller, G., Hinterberger, T., et al.: The bci competition 2003: progress and perspectives in detection and discrimination of eeg single trials. IEEE Transactions on Biomedical Engineering 51(6), 1044–1051 (2004)
Chen, Y., Akutagawa, M., Katayama, M., Zhang, Q., Kinouchi, Y.: Ica based multiple brain sources localization. In: Conf. Proc. IEEE Eng. Med. Biol. Soc. 2008, pp. 1879–1882 (2008)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning, 273–297 (1995)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (2010)
Gramfort, A., Strohmeier, D., Haueisen, J., Hamalainen, M., Kowalski, M.: Functional brain imaging with m/eeg using structured sparsity in time-frequency dictionaries. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 600–611. Springer, Heidelberg (2011)
Held, K., Kops, E., Krause, B., Wells III, W., Kikinis, R., Muller-Gartner, H.: Markov random field segmentation of brain MR images. IEEE Transactions on Medical Imaging 16(6), 878–886 (1997)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley-Interscience (2001)
Joachims, T., Finley, T., Yu, C.N.J.: Cutting-plane training of structural svms. Machine Learning 77(1), 27–59 (2009)
Jung, T., Makeig, S., Mckeown, M.J., Bell, A.J., won Lee, T., Sejnowski, T.J.: Imaging brain dynamics using independent component analysis. Proceedings of the IEEE 89, 1107–1122 (2001)
Listgarten, J., Neal, R.M., Roweis, S.T., Emili, A.: Multiple alignment of continuous time series. In: Advances in Neural Information Processing Systems, pp. 817–824. MIT Press (2005)
Polich, J.: Updating p300: An integrative theory of P3a and P3b. Clinical Neurophysiology 118(10), 2128 (2007)
Sriperumbudur, B., Lanckriet, G.: On the convergence of the concave-convex procedure. In: Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 22, pp. 1759–1767 (2009)
Thulasidas, M., Guan, C., Wu, J.: Robust classification of EEG signal for brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(1), 24–29 (2006)
Woody, C.: Characterization of an adaptive filter for the analysis of variable latency neuroelectrical signals. Medical and Biological Engineering 5, 539–553 (1967)
Yu, C.N.J., Joachims, T.: Learning structural SVMs with latent variables. In: Proceedings of the International Conference on Machine Learning, ICML (2009)
Yuille, A.L., Rangarajan, A.: The concave-convex procedure. Neural Computation 15(4), 915–936 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zaremba, W., Kumar, M.P., Gramfort, A., Blaschko, M.B. (2013). Learning from M/EEG Data with Variable Brain Activation Delays. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds) Information Processing in Medical Imaging. IPMI 2013. Lecture Notes in Computer Science, vol 7917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38868-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-38868-2_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38867-5
Online ISBN: 978-3-642-38868-2
eBook Packages: Computer ScienceComputer Science (R0)