Abstract
The under-sample classification problem is discussed for 21 normal childrenand 21 children with reading disability. We first rejected data of one subject in each group and produced 441 sub-datasets including 40 subjects in each. Regarding each sub-dataset, we extracted features through nonnegative Tucker decomposition (NTD) from event-related potentials, and used the leave-one-out paradigm for classification. Averaged accuracies over 441 sub-datasets were 77.98% (linear discriminate analysis), 73.55% (support vector machine), and 76.97% (adaptive boosting). In summary, assuming K observations with known labels, for the new observation without the group information, the feature of the new observation can be extracted through performing NTD to extract features from data of all observations (K+1). Since the group information of the first K observations is known, their features can train the classifier, and then, the trained classifier recognizes new features to determine the group information of new observation.
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
Preview
Unable to display preview. Download preview PDF.
References
Bishop, C.M.: Pattern recognition and machine learning, 1st edn. Springer, Singapore (2006)
Cichocki, A., Zdunek, R., Phan, A.H., et al.: Nonnegative matrix and tensor factorizations: Applications to exploratory multi-way data analysis. John Wiley (2009)
Cong, F., Huang, Y., Kalyakin, I., et al.: Frequency Response Based Wavelet Decomposition to Extract Children’s Mismatch Negativity Elicited by Uninterrupted Sound. J. Med. Biol. Eng. 32, 205–214 (2012)
Cong, F., Kalyakin, I., Li, H., et al.: Answering Six Questions in Extracting Children’s Mismatch Negativity through Combining Wavelet Decomposition and Independent Component Analysis. Cogn. Neurodynamics 5, 343–359 (2011)
Cong, F., Kalyakin, I., Ristaniemi, T.: Can Back-Projection Fully Resolve Polarity Indeterminacy of ICA in Study of ERP? Biomed. Signal Process. 6, 422–426 (2011)
Cong, F., Kalyakin, I., Zheng, C., et al.: Analysis on Subtracting Projection of Extracted Independent Components from EEG Recordings. Biomed. Tech. 56, 223–234 (2011)
Cong, F., Phan, A.H., Astikainen, P., Zhao, Q., Hietanen, J.K., Ristaniemi, T., Cichocki, A.: Multi-domain Feature of Event-Related Potential Extracted by Nonnegative Tensor Factorization: 5 vs. 14 Electrodes EEG Data. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds.) LVA/ICA 2012. LNCS, vol. 7191, pp. 502–510. Springer, Heidelberg (2012)
Cong, F., Phan, A.H., Lyytinen, H., Ristaniemi, T., Cichocki, A.: Classifying Healthy Children and Children with Attention Deficit through Features Derived from Sparse and Nonnegative Tensor Factorization Using Event-Related Potential. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds.) LVA/ICA 2010. LNCS, vol. 6365, pp. 620–628. Springer, Heidelberg (2010)
Cong, F., Kalyakin, I., Huttunen-Scott, T., et al.: Single-Trial Based Independent Component Analysis on Mismatch Negativity in Children. Int. J. Neural Syst. 20, 279–292 (2010)
Cong, F., Phan, A.H., Zhao, Q., Nandi, A.K., Alluri, V., Toiviainen, P., Poikonen, H., Huotilainen, M., Cichocki, A., Ristaniemi, T.: Analysis of Ongoing EEG Elicited by Natural Music Stimuli Using Nonnegative Tensor Factorization. In: Proceeding of The 2012 European Signal Processing Conference (EUSIPCO 2012), Bucharest, Romania, August 27-31, pp. 494–498 (2012)
Duncan, C.C., Barry, R.J., Connolly, J.F., et al.: Event-Related Potentials in Clinical Research: Guidelines for Eliciting, Recording, and Quantifying Mismatch Negativity, P300, and N400. Clin. Neurophysiol. 120, 1883–1908 (2009)
Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)
Huttunen, T., Halonen, A., Kaartinen, J., et al.: Does Mismatch Negativity show Differences in Reading-Disabled Children Compared to Normal Children and Children with Attention Deficit? Dev. Neuropsychol. 31, 453–470 (2007)
Kalyakin, I., Gonzalez, N., Joutsensalo, J., et al.: Optimal Digital Filtering Versus Difference Waves on the Mismatch Negativity in an Uninterrupted Sound Paradigm. Dev. Neuropsychol. 31, 429–452 (2007)
Leppanen, P.H., Lyytinen, H.: Auditory Event-Related Potentials in the Study of Developmental Language-Related Disorders. Audiol. Neurootol. 2, 308–340 (1997)
Luck, S.J.: An Introduction to the Event-Related Potential Technique. The MIT Press, Cambridge (2005)
Lyytinen, H., Guttorm, T.K., Huttunen, T., et al.: Psychophysiology of Developmental Dyslexia: A Review of Findings Including Studies of Children at Risk for Dyslexia. J. Neurolinguist. 18, 167–195 (2005)
Makeig, S., Jung, T.P., Bell, A.J., et al.: Blind Separation of Auditory Event-Related Brain Responses into Independent Components. Proc. Natl. Acad. Sci. U.S.A. 94, 10979–10984 (1997)
Makeig, S., Westerfield, M., Jung, T.P., et al.: Functionally Independent Components of the Late Positive Event-Related Potential during Visual Spatial Attention. J. Neurosci. 19, 2665–2680 (1999)
Näätänen, R.: Attention and Brain Functions. Lawrence Erlbaum Associates, Hillsdale (1992)
Näätänen, R., Gaillard, A.W., Mantysalo, S.: Early Selective-Attention Effect on Evoked Potential Reinterpreted. Acta. Psychol. (Amst) 42, 313–329 (1978)
Näätänen, R., Kujala, T., Kreegipuu, K., et al.: The Mismatch Negativity: An Index of Cognitive Decline in Neuropsychiatric and Neurological Diseases and in Ageing. Brain 134, 3432–3450 (2011)
Näätänen, R., Kujala, T., Winkler, I.: Auditory Processing that Leads to Conscious Perception: A Unique Window to Central Auditory Processing Opened by the Mismatch Negativity and Related Responses. Psychophysiology 48, 4–22 (2011)
Niedermeyer, E., Lopes da Silva, F.: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Williams & Wilkins, Baltimore (2004)
Phan, A.H., Cichocki, A.: Extended HALS Algorithm for Nonnegative Tucker Decomposition and its Applications for Multiway Analysis and Classification. Neurocomputing 74, 1956–1969 (2011)
Phan, A.H., Cichocki, A.: Tensor Decomposition for Feature Extraction and Classification Problem. IEICE T, Fund. Electr. 1, 37–68 (2010)
Phan, A.H., Tichavsky, P., Cichocki, A.: Damped Gauss-Newton Algorithm for Nonnegative Tucker Decomposition, pp. 665–668 (2011)
Tallon-Baudry, C., Bertrand, O., Delpuech, C., et al.: Stimulus Specificity of Phase-Locked and Non-Phase-Locked 40 Hz Visual Responses in Human. J. Neurosci. 16, 4240–4249 (1996)
Tan, D.S., Nijholt, A.: Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction. In: Anonymous, p. 277. Springer, London (2010)
Tao, D.C., Li, X.L., Wu, X.D., et al.: Supervised Tensor Learning. Knowl. Inf. Syst. 13, 1–42 (2007)
Tao, D.C., Li, X.L., Wu, X.D., et al.: General Tensor Discriminant Analysis and Gabor Features for Gait Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1700–1715 (2007)
Zhang, Y., Zhao, Q., Jin, J., et al.: A Novel BCI Based on ERP Components Sensitive to Configural Processing of Human Faces. J. Neural Eng. 9, 026018 (2012)
Zhao, Q., Rutkowski, T.M., Zhang, L., et al.: Generalized Optimal Spatial Filtering using a Kernel Approach with Application to EEG Classification. Cogn. Neurodyn. 4, 355–358 (2010)
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
Cong, F., Phan, A.H., Zhao, Q., Wu, Q., Ristaniemi, T., Cichocki, A. (2012). Feature Extraction by Nonnegative Tucker Decomposition from EEG Data Including Testing and Training Observations. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-34487-9_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34486-2
Online ISBN: 978-3-642-34487-9
eBook Packages: Computer ScienceComputer Science (R0)