Advanced EEG Signal Processing in Brain Death Diagnosis
暂无分享,去创建一个
[1] Ernst Fernando Lopes Da Silva Niedermeyer,et al. Electroencephalography, basic principles, clinical applications, and related fields , 1982 .
[2] S M Pincus,et al. Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.
[3] Jean-François Cardoso,et al. Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..
[4] Maria G. Knyazeva,et al. Assessment of EEG synchronization based on state-space analysis , 2005, NeuroImage.
[5] S. Marks,et al. Apneic oxygenation in apnea tests for brain death. A controlled trial. , 1990, Archives of neurology.
[6] Andrzej Cichocki,et al. Independent component analysis for unaveraged single-trial MEG data decomposition and single-dipole source localization , 2002, Neurocomputing.
[7] Andrzej Cichocki,et al. A robust approach to independent component analysis of signals with high-level noise measurements , 2003, IEEE Trans. Neural Networks.
[8] Antoine Souloumiac,et al. Jacobi Angles for Simultaneous Diagonalization , 1996, SIAM J. Matrix Anal. Appl..
[9] 曹 建庭,et al. Analysis of the quasi-brain-death EEG data based on a robust ICA approach , 2006 .
[10] Shun-ichi Amari,et al. Natural Gradient Learning for Over- and Under-Complete Bases in ICA , 1999, Neural Computation.
[11] Zhe Chen,et al. An Empirical Quantitative EEG Analysis for Evaluating Clinical Brain Death , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[12] Schuster,et al. Separation of a mixture of independent signals using time delayed correlations. , 1994, Physical review letters.
[13] S. Pincus. Approximate entropy (ApEn) as a complexity measure. , 1995, Chaos.
[14] Terrence J. Sejnowski,et al. Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.
[15] A. Cichocki,et al. An empirical EEG analysis in brain death diagnosis for adults , 2008, Cognitive Neurodynamics.
[16] Toshihisa Tanaka,et al. Complex Empirical Mode Decomposition , 2007, IEEE Signal Processing Letters.
[17] E. Wijdicks,et al. Brain death worldwide , 2002, Neurology.
[18] C M Shapiro,et al. Aplastic anemia associated with ticlopidine , 1996, Neurology.
[19] C. Peng,et al. Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[20] M.A. Lin,et al. Linear and Nonlinear EEG Indexes in Relation to the Severity of Coma , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.
[21] Andrzej Cichocki,et al. A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.
[22] R. Taylor,et al. Reexamining the Definition and Criteria of Death , 1997, Seminars in neurology.
[23] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[24] Aapo Hyvärinen,et al. A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.
[25] Patrick Brézillon,et al. Lecture Notes in Artificial Intelligence , 1999 .
[26] Fang Chen,et al. Dynamic process of information transmission complexity in human brains , 2000, Biological Cybernetics.
[27] Tzyy-Ping Jung,et al. Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.
[28] S. J. Roberts,et al. Temporal and spatial complexity measures for electroencephalogram based brain-computer interfacing , 2006, Medical & Biological Engineering & Computing.
[29] Xin Meng,et al. Can We Measure Consciousness with EEG Complexities? , 2003, Int. J. Bifurc. Chaos.
[30] Alexander J. Smola,et al. Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.