Brain tumor classification based on EEG hidden dynamics

[1]  John G. Kemeny,et al.  Finite Markov Chains. , 1960 .

[2]  F H Duffy,et al.  Brain electrical activity mapping (BEAM): A method for extending the clinical utility of EEG and evoked potential data , 1979, Annals of neurology.

[3]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[4]  C. Gardiner Handbook of Stochastic Methods , 1983 .

[5]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[6]  A. Babloyantz,et al.  Evidence of Chaotic Dynamics of Brain Activity During the Sleep Cycle , 1985 .

[7]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[8]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[9]  S. Park,et al.  TDAT-time domain analysis tool for EEG analysis , 1990, IEEE Transactions on Biomedical Engineering.

[10]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[11]  G. Buzsáki The thalamic clock: Emergent network properties , 1991, Neuroscience.

[12]  M. Steriade,et al.  Network modulation of a slow intrinsic oscillation of cat thalamocortical neurons implicated in sleep delta waves: cortically induced synchronization and brainstem cholinergic suppression , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[13]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[14]  W. Ditto,et al.  Controlling chaos in the brain , 1994, Nature.

[15]  W. Pritchard,et al.  Dimensional analysis of resting human EEG. II: Surrogate-data testing indicates nonlinearity but not low-dimensional chaos. , 1995, Psychophysiology.

[16]  D. Lehmann,et al.  Segmentation of brain electrical activity into microstates: model estimation and validation , 1995, IEEE Transactions on Biomedical Engineering.

[17]  H. Kantz,et al.  Dimension estimates and physiological data. , 1995, Chaos.

[18]  Milan Palus,et al.  Nonlinearity in normal human EEG: cycles, temporal asymmetry, nonstationarity and randomness, not chaos , 1996, Biological Cybernetics.

[19]  G. Deco,et al.  Exploring the intrinsic information loss in single-humped maps by refining multi-symbol partitions , 1996 .

[20]  D. K. Ivanov,et al.  Statistical measures derived from the correlation integrals of physiological time series. , 1996, Chaos.

[21]  N. Thakor,et al.  Dominant frequency analysis of EEG reveals brain's response during injury and recovery , 1996, IEEE Transactions on Biomedical Engineering.

[22]  G. Deco,et al.  Information Flow in Chaotic Symbolic Dynamics for Finite and Infinitesimal Resolution , 1997 .

[23]  Gustavo Deco,et al.  Dynamics extraction in multivariate biomedical time series , 1998, Biological Cybernetics.