Abstract
In this paper we apply a novel tensor decomposition model of SOD (slice oriented decomposition) to extract slice features from the multichannel time-frequency representation of EEG signals measured for MI (motor imagery) tasks in application to BCI (brain computer interface). The advantages of the SOD based feature extraction approach lie in its capability to obtain slice matrix components across the space, time and frequency domains and the discriminative features across different classes without any prior knowledge of the discriminative frequency bands. Furthermore, the combination of horizontal, lateral and frontal slice features makes our method more robust for the outlier problem. The experiment results demonstrate the effectiveness of our method.
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References
Smilde, A.K., Bro, R., Geladi, P.: Multi-way Analysis with Applications in the Chemical Sciences. Wiley, Chichester (2004)
Heiler, M., Schnörr, C.: Controlling Sparseness in Non-negative Tensor Factorization. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 56–67. Springer, Heidelberg (2006)
Cichocki, A., Zdunek, R., Choi, S., Plemmons, R., Amari, S.: Non-Negative Tensor Factorization using Alpha and Beta Divergences. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2007. ICASSP 2007, vol. 3 (2007)
Mørup, M., Hansen, L., Herrmann, C., Parnas, J., Arnfred, S.: Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG. Neuroimage 29(3), 938–947 (2006)
Miwakeichi, F., Martínez-Montes, E., Valdés-Sosa, P., Nishiyama, N., Mizuhara, H., Yamaguchi, Y.: Decomposing EEG data into space–time–frequency components using Parallel Factor Analysis. Neuroimage 22(3), 1035–1045 (2004)
Cichocki, A., Zdunek, R., Phan, A.H., Amari, S.: Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. Wiley, Chichester (2009)
Mørup, M., Hansen, L., Arnfred, S.: ERPWAVELAB A toolbox for multi-channel analysis of time–frequency transformed event related potentials. Journal of Neuroscience Methods 161(2), 361–368 (2007)
Yang, J., Zhang, D., Frangi, A., Yang, J.Y.: Two-dimensional pca: A new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)
Tao, D., Li, X., Wu, X., Maybank, S.: General Tensor Discriminant Analysis and Gabor Features for Gait Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(10), 1700–1715 (2007)
Ye, J., Janardan, R., Li, Q.: Two-dimensional linear discriminant analysis. In: NIPS (2004)
Wang, X., Tang, X.: A unified framework for subspace face recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 26(9), 1222–1228 (2004)
Fu, Y., Huang, T.: Image classification using correlation tensor analysis. IEEE Transaction on Image Processing 17(2), 226–234 (2008)
He, X., Cai, D., Niyogi, P.: Tensor subspace analysis. In: NIPS 2006 (2006)
Tao, D., Li, X., Hu, W., Maybank, S., Wu, X.: Supervised tensor learning. In: Proceedings of the Fifth IEEE International Conference on Data Mining, ICDM 2005 (2007)
Caiafa, C.F., Cichocki, A.: Slice Oriented Decomposition (SOD): A New Tensor Decomposition for Representation of 3-way Data (submitted February 2009)
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Zhao, Q., Caiafa, C.F., Cichocki, A., Zhang, L., Phan, A.H. (2009). Slice Oriented Tensor Decomposition of EEG Data for Feature Extraction in Space, Frequency and Time Domains. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_25
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DOI: https://doi.org/10.1007/978-3-642-10677-4_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10676-7
Online ISBN: 978-3-642-10677-4
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