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Books on the topic 'Blind source separation'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 July 2024

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1

Xiang, Yong, Dezhong Peng, and Zuyuan Yang. Blind Source Separation. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-227-2.

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2

Naik, Ganesh R., and Wenwu Wang, eds. Blind Source Separation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4.

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3

Yu, Xianchuan, Dan Hu, and Jindong Xu. Blind Source Separation. Singapore: John Wiley & Sons, Singapore Pte. Ltd, 2014. http://dx.doi.org/10.1002/9781118679852.

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4

Deville, Yannick, Leonardo Tomazeli Duarte, and Shahram Hosseini. Nonlinear Blind Source Separation and Blind Mixture Identification. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64977-7.

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5

Bourgeois, Julien, and Wolfgang Minker, eds. Time-Domain Beamforming and Blind Source Separation. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-68836-7.

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6

Shi, Xizhi. Blind signal processing: Theory and practice. Shanghai: Shanghai Jiao Tong University Press, 2011.

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7

Comon, Pierre. Handbook of Blind Source Separation: Independent Component Analysis and Applications. Burlington: Elsevier, 2010.

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8

author, Sun Jiande, and Xu Hongji author, eds. Mang xin hao chu li li lun yu ying yong. Beijing: Ke xue chu ban she, 2013.

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9

Mang xin hao chu li ji chu ji qi ying yong: Blind signal processing foundation and it's applications. Beijing Shi: Guo fang gong ye chu ban she, 2010.

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10

C, Loizou Philipos, ed. Advances in modern blind signal separation algorithms: Theory and applications. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2010.

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11

Zhen dong xin hao de mang yuan fen li ji shu ji ying yong. Beijing Shi: Hang kong gong ye chu ban she, 2011.

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12

Andrzej, Cichocki, ed. Non-negative matrix and tensor factorizations: Applications to exploratory multiway data analysis and blind source separation. Hoboken, NJ: John Wiley, 2009.

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13

author, Feng Jiuchao 1964, and Xie Zhigang 1965-, eds. Chuan gan qi wang luo zhong de mang yuan fen li yu xin hao chong gou: Blind Source Separation and Signal Reconstruction in Sensor Networks. Beijing Shi: Dian zi gong ye chu ban she, 2012.

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14

E, Davies Mike, ed. Independent component analysis and signal separation: 7th international conference, ICA 2007, London, UK, September 9-12, 2007 : proceedings. Berlin: Springer, 2007.

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15

Handbook of Blind Source Separation. Elsevier, 2010. http://dx.doi.org/10.1016/c2009-0-19334-0.

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16

Duarte, Leonardo Tomazeli, Shahram Hosseini, and Yannick Deville. Nonlinear Blind Source Separation and Blind Mixture Identification. Springer International Publishing AG, 2021.

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17

Xu, Jindong, Dan Hu, and Xianchuan Yu. Blind Source Separation: Theory and Applications. Wiley & Sons, Limited, John, 2014.

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18

Xiang, Yong, Dezhong Peng, and Zuyuan Yang. Blind Source Separation: Dependent Component Analysis. Springer London, Limited, 2014.

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19

Xu, Jindong, Dan Hu, and Xianchuan Yu. Blind Source Separation: Theory and Applications. Wiley, 2014.

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20

Xu, Jindong, Dan Hu, and Xianchuan Yu. Blind Source Separation: Theory and Applications. Wiley & Sons, Incorporated, John, 2013.

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21

Xu, Jindong, Dan Hu, and Xianchuan Yu. Blind Source Separation: Theory and Applications. Wiley & Sons, Incorporated, John, 2013.

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22

Haykin, Simon. Unervised Adaptive Filtering, Blind Source Separation. Wiley & Sons, Incorporated, John, 2008.

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23

Xu, Jindong, Dan Hu, and Xianchuan Yu. Blind Source Separation: Theory and Applications. Wiley & Sons, Incorporated, John, 2013.

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24

Xiang, Yong, Dezhong Peng, and Zuyuan Yang. Blind Source Separation: Dependent Component Analysis. Springer, 2014.

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25

Haykin, Simon. Unsupervised Adaptive Filtering, Volume 1: Blind Source Separation. Wiley-Interscience, 2000.

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26

Shi, Xizhi. Blind Signal Processing. Springer, 2012.

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27

Naik, Ganesh R., and Wenwu Wang. Blind Source Separation: Advances in Theory, Algorithms and Applications. Springer, 2014.

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28

Naik, Ganesh R., and Wenwu Wang. Blind Source Separation: Advances in Theory, Algorithms and Applications. Springer Berlin / Heidelberg, 2016.

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29

Naik, Ganesh R., and Wenwu Wang. Blind Source Separation: Advances in Theory, Algorithms and Applications. Springer, 2014.

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30

Naik, Ganesh R., and Wenwu Wang. Blind Source Separation: Advances in Theory, Algorithms and Applications. Springer London, Limited, 2014.

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31

Almeida, Luis B. Nonlinear Source Separation (Synthesis Lectures on Signal Processing). Morgan & Claypool Publishers, 2006.

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32

Handbook Of Blind Source Separation Independent Component Analysis And Applications. Academic Press, 2010.

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33

Girolami, Mark. Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation. Springer London, Limited, 2012.

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34

Dublin, University College, ed. Underdetermined blind source separation in echoic environments using linear arrays & sparse representations. 2007.

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35

Calhoun, Vince, and Tulay Adali. Brain Image Analysis Using Blind Source Separation: Decoding and Explaining the Brain. Elsevier Science & Technology Books, 2021.

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36

Loizou, Philipos, and Kostas Kokkinakis. Advances in Modern Blind Signal Separation Algorithms: Theory and Applications. Springer International Publishing AG, 2010.

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37

Bourgeois, Julien, and Wolfgang Minker. Time-Domain Beamforming and Blind Source Separation: Speech Input in the Car Environment. Springer, 2009.

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38

Bourgeois, Julien, and Wolfgang Minker. Time-Domain Beamforming and Blind Source Separation: Speech Input in the Car Environment. Springer, 2010.

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39

Girolami, Mark. Self-Organizing Neural Networks: Independent Component Analysis and Blind Source Separation (Perspectives in Neural Computing). Springer-Verlag, 1999.

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40

Amari, Shun-Ichi, Rafal Zdunek, Anh Huy Phan, and Andrzej Cichocki. Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation. Wiley & Sons, Incorporated, John, 2009.

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41

Time-Domain Beamforming and Blind Source Separation: Speech Input in the Car Environment (Lecture Notes Electrical Engineering). Springer, 2007.

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42

Amari, Shun-Ichi, Rafal Zdunek, Anh Huy Phan, and Andrzej Cichocki. Non-Negative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation. Wiley & Sons, Limited, John, 2009.

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43

Congendo, Marco, and Fernando H. Lopes da Silva. Event-Related Potentials. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0039.

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Event-related potentials (ERPs) can be elicited by a variety of stimuli and events in diverse conditions. This chapter covers the methodology of analyzing and quantifying ERPs in general. Basic models (additive, phase modulation and resetting, potential asymmetry) that account for the generation of ERPs are discussed. The principles and requirements of ensemble time averaging are presented, along with several univariate and multivariate methods that have been proposed to improve the averaging procedure: wavelet decomposition and denoising, spatial, temporal and spatio-temporal filtering. We emphasize basic concepts of principal component analysis, common spatial pattern, and blind source separation, including independent component analysis. We cover practical questions related to the averaging procedure: overlapping ERPs, correcting inter-sweep latency and amplitude variability, alternative averaging methods (e.g., median), and estimation of ERP onset. Some specific aspects of ERP analysis in the frequency domain are surveyed, along with topographic analysis, statistical testing, and classification methods.
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44

Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.
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