Dyrholm M, Makeig S, Hansen LK. (2007). Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG. Neural computation. 19 [PubMed]

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References and models cited by this paper

Amari S, Cichocki A, Douglas SC. (1999). Self-whitening algorithms for adaptive equalization and deconvolution IEEE Trans Signal Processing. 47

Anemüller J, Sejnowski TJ, Makeig S. (2003). Complex independent component analysis of frequency-domain electroencephalographic data. Neural networks : the official journal of the International Neural Network Society. 16 [PubMed]

Attias H, Schreiner CE. (1998). Blind source separation and deconvolution: the dynamic component analysis algorithm. Neural computation. 10 [PubMed]

Bell AJ, Lee TW, Lambert RH. (1997). Blind separation of convolved and delayed sources. Advances in Neural Information Processing Systems.. 9

Bell AJ, Lee TW, Orglmeister R. (1997). Blind source separation of real world signals International Conference Neural Networks.

Bell AJ, Sejnowski TJ. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural computation. 7 [PubMed]

Cardoso JF, Belouchrani A, Karim AM, Moulines E. (1997). A blind source separation technique using second-order statistics IEEE Trans Signal Processing. 45

Cardoso JF, Gassiat E, Moulines E. (1997). Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models International Conference on Acoustics, Speech, and Signal Processing.

Cardoso JF, Pham DT. (2004). Optimization issues in noisy gaussian ICA Independent component analysis and blind signal separation.

Choi S, Cichocki A. (1997). Blind signal deconvolution by spatio-temporal decorrelation and demixing Neural Networks for signal processing.

Choi S, Cichocki A, Amari SI, Wen_Liu RW. (1999). Natural gradient learning with a nonholonomic constraint for blind deconvolution of multiple channels Independent Component Analysis and Blind Signal Separation.

Comon P, Moreau E, Rota L. (2001). Blind separation of convolutive mixtures: A contrast based joint diagonalization approach Independent Component Analysis and Blind Source Separation.

Davies M, Mitianoudis N. (2003). Audio source separation of convolutive mixtures IEEE Trans Speech And Audio Processing. 11

Deligne S, Gopinath R. (2002). An EM algorithm for convolutive independent component analysis Neurocomputing. 49

Delorme A, Makeig S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods. 134 [PubMed]

Efron B, Tibshirani R. (1993). An Introduction To The Bootstrap.

Hansen LK, Dyrholm M. (2004). CICAAR: Convolutive ICA with an autoregressive inverse model Independent Component Analysis and Blind Signal Separation.

Hansen PC. (2002). Deconvolution and regularization with Toeplitz matrices Numerical Algorithms. 29

Jung TP et al. (2001). Imaging Brain Dynamics Using Independent Component Analysis. Proceedings of the IEEE. Institute of Electrical and Electronics Engineers. 89 [PubMed]

Kollmeier B, Anemuller J. (2003). Adaptive separation of acoustic sources for anechoic conditions: A constrained frequency domain approach IEEE Trans Speech And Audio Processing. 39

Lee TW, Girolami M, Sejnowski TJ. (1999). Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural computation. 11 [PubMed]

Makeig S, Debener S, Onton J, Delorme A. (2004). Mining event-related brain dynamics. Trends in cognitive sciences. 8 [PubMed]

Makeig S et al. (2004). Electroencephalographic brain dynamics following manually responded visual targets. PLoS biology. 2 [PubMed]

Makeig S, Enghoff S, Jung TP, Sejnowski TJ. (2000). A natural basis for efficient brain-actuated control. IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 8 [PubMed]

Makeig S, Hansen LK, Dyrholm M. (2006). Model structure selection in convolutive mixtures Independent Component Analysis and Blind Signal Separation.

Makeig S et al. (2002). Dynamic brain sources of visual evoked responses. Science (New York, N.Y.). 295 [PubMed]

Nielsen HB. (2000). UCMINF an algorithm for unconstrained, nonlinear optimization Tech Rep IMM-REP-2000-19, Department of Mathematical Modeling,Technical University of Denmark.

Onton J, Delorme A, Makeig S. (2005). Frontal midline EEG dynamics during working memory. NeuroImage. 27 [PubMed]

Orglmeister R, Baumann W, Kohler BU, Kolossa D. (2001). Real time separation of convolutive mixtures 3rd Intl Conf Independent Component Analysis and Blind Signal Separation.

Parra LC, Pearlmutter BA. (1997). Maximum likelihood blind source separation:A context-sensitive generalization of ICA Advances in neural information processing systems. 9

Rahbar K, Reilly J. (2001). Blind source separation of convolved sources by joint approximate diagonalization of cross-spectral density matrices Intl Conf Acoustics, Speech, and Signal Processing.

Reilly JP, Rahbar K, Manton JH. (2002). A frequency domain approach t oblind identification of mimo FIR systems driven by quasi-stationary signals Intl Conf Acoustics, Speech, and Signal Processing.

Schneider T, Neumaier A. (2001). Estimation of parameters and eigenmodes of multivariate autoregressive models ACM Trans Mathematical Software. 27

Schwarz G. (1978). Estimating the dimension of a model Ann Stat. 6

Sejnowski TJ, Bell A, Makeig S, Jung TP. (1996). Independent component analysis of electroencephalographic data Advances in neural information processing systems.

Sejnowski TJ, Makeig S, Anemuller J, Duann JR. (2004). Unraveling spatiotemporal dynamics in FMRI recordings using complex ICA Independent component analysis and blind signal separation.

Sejnowski TJ, Makeig S, Delorme A. (2002). From single-trial EEG to brain area dynamics Neurocomputing. 44

Sejnowski TJ et al. (1998). Extended ICA removes artifacts from electroencephalographic recordings Advances in neural information processing systems. 10

Sejnowski TJ et al. (2000). Psychophysiology. 37

Spence C, Parra L. (2000). Convolutive blind source separation of nonstationary sources IEEE Trans Speech Audio Process. 8

Spence C, Parra L, Vries B. (1997). Convolutive source separation and signal modeling with Ml International Symposium on Intelligent Systems.

Spence C, Parra L, Vries BD. (1998). Convolutive blind source separation based on multiple decorrelation Neural Networks for Signal Processing Proceedings.

Sun X, Douglas S. (2001). A natural gradient convolutive blind source separation algorithms for speech mixtures Independent Component Analysis and Blind Source Separation.

Torkkola K. (1996). Blind separation of convolved sources based on information maximization. IEEE Workshop On Neural Networks For Signal Processing Kyoto, Japan.

Wang L, Arendt-Nielsen L, Chen AC, Hansen LK, Dyrholm M. (2004). Convolutive ICA (c-ICA) captures complex spatio-temporal EEG activity Available online at http:--www.imm.dtu.dk-mad-papers-cICA eeg hbm2004.pdf.

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