Advanced EEG Signal Processing in Brain Death Diagnosis

[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.