Quantitative analysis of EEG signals: Time-frequency methods and ...

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References Abarbanel HDI, Brown R, Sidorowich JJ and Tsimring LS. Rev. Mod. Phys. 65, 1331, 1993. Abraham R and Shaw C. Dynamics - The geometry of behavior. Part 2: Chaotic behavior. Aerial Press, California, 1983 Achermann P, Hartmann R, Gunzinger A, Guggenbuhl W and Borbely A. All night sleep EEG and articial stochastic control signals have similar correlation dimensions. Electr. Clin. Neurophysiol., 1994a 90: 384-387. Achermann P, Hartmann R, Gunzinger A, Guggenbuhl W and Borbely A. Correlation dimension of the human sleep electroencephalogram: cyclic changes in the course of the night. Eur. J. Neurosc. 1994b 6: 497-500. Ademoglu A, Micheli-Tzanakou E and Istefanopulos Y. Analysis of pattern reversal visual evoked potentials by spline wavelets. IEEE Trans.Biom.Eng., 1997 44:881-890 Adrian ED. Aerent discharges to the cerebral cortex from peripheral sense organs. JPhysiol, 1941, 100:159-191. Akay M, Akay YM, Cheng P and Szeto HH. Time-frequency analysis of the electrocortical activity during maturation using wavelet transform. Biol. Cybern., 1994 71: 169-176. Albano, M., Muench, J., Schwartz, C., Mees, A., and Rapp, P., Singular Value Descomposition and the Grassberger-Procaccia Algorithm. Phys. Rev. A, 38, 0, 1988. Andersen P, Andersson SA. Physiological Basis of the Alpha Rhythm. New York: Appleton-Century- Crofts, 1968. Arnhold J, Grassberger P, Lehnertz K and Elger C. A robust method for detecting interdependencies: Application intracranially recorded EEG. Physica D, 1999 (in press). Arroyo S,Lesser R, Gordon B, Fisher R and Uematsu S. Subdural electrodes. In: Niedermeyer, E., and F. H. Lopes da Silva (eds). Electroencephalography: Basic Principles, Clinical Applications and Related Fields. Baltimore: Williams and Wilkins 3rd ed., 1993, pp: 701-711. Babloyantz A, Salazar JM and Nicolis C. Evidence of chaotic dynamics of brain activity during the sleep cycle. Phys. Lett. 3:152-156 1985 Babloyantz A. Evidence of chaotic dynamics of brain activity during the sleep cycle. In: GMeyer-Kress (Ed.), Dimesions and entropies in chaotic systems. Springer, Berlin, 1986, pp:241-245. Babloyantz A and Destexhe A. Low-dimensional chaos in an instance of epilepsy. Proc. Natl. Acad. Sci. 83: 3513-3517 1986. Babloyantz A. Chaotic Dynamics in Brain Activity. In: Erol Basar (ed.) Dynamics of Sensory and Cognitive Processing by the Brain. Springer Verlag, Berlin 1988. Bal T, Von Krosigk M and McCormick D. Synaptic and membrane mechanisms underlying synchronized oscillations in the ferret LGNd in vitro. J. Physiol. Lond. 1995a, 483:641-663. Bal T, Von Krosigk M and McCormick D. Role of the ferret perigeniculate nucleus in the generation of synchronized oscillations in vitro. J. Physiol. Lond. 1995b, 483:665-685. 117
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Quantitative analysis of EEG signal
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1. Berichterstatter: Prof. Dr.-Ing.
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an okzipitalen Positionen und (c) d
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To my family: Mama, Consuelo, Hugui
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Preface In this work, I will descri
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I would certainly like to remember
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Contents Zusammenfassung Preface Ac
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5.2.6 Calculating Lyapunov Exponent
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Summary Since the rsts recordings i
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1 Outline of Neurophysiology: Brain
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Figure 2: Normal 20 channels (10-20
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1.1.2 EEG in Epilepsy One important
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Figure 3: Scalp EEG of 18 channels
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Figure 4: Averaged responses upon N
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2 Fourier Transform 2.1 Introductio
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I xx (k) =jX(k)j 2 = X(k) X (k) (
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17 Figure 5: Topographical mapping
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1987) is that coherence gives the c
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3 Gabor Transform (Short Time Fouri
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where k g D k= R 1 ;1 jg D(t 0 )j 2
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and the band maximum peak frequency
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Figure 7: Intracraneal seizure reco
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Figure 10: Mean (soft line) and max
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EEG as the one with least amount of
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Figure 11: Scalp EEG of the right c
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3.5 Conclusion In this chapter I de
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Original signal FOURIER TRANSFORM G
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4.2.3 Multiresolution Analysis Cont
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Original signal -15 0 15 -1sec 0 1s
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4.2.5 Wavelet Packets In the approa
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marked decrease in computational ti
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ain activity during an epileptic se
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3 2 3 3 3 3.2 Hz 3.6 Hz 4.0 Hz 4.4
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Silva et al.,1973a,1973b Lopes da S
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sweep #1 sweep #2 sweep #3 sweep #4
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VEP non-target target F3 F4 F3 F4 F
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VEP non-target target F3 F4 F3 F4 F
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Electrode F3 F4 Cz P3 P4 O1 O2 Dela
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In conclusion, our results point to
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63 Figure 22: Gamma responses for a
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Figure 24: T-test comparison of the
Page 84 and 85: wavelet coecients allowed an easy d
Page 86 and 87: the position vs. the linear momentu
Page 88 and 89: unknown topology it is necessary to
Page 90 and 91: the sum of the positive exponents (
Page 92 and 93: performed. On one hand, must be lar
Page 94 and 95: Duke (1992) and Elbert et al. (1994
Page 96 and 97: agation time. They applied this pro
Page 98 and 99: Figure 28: Histogram for the EEG da
Page 100 and 101: Figure 29: Attractors corresponding
Page 102 and 103: able for automatic detection proces
Page 104 and 105: ENTROPY Thermodynamics Signal analy
Page 106 and 107: 6.3 Application to visual event-rel
Page 108 and 109: VEP Non Target Target F3 -20 -10 0
Page 110 and 111: 1 VEP NON-TARGET TARGET 1 1 F3 F3 0
Page 112 and 113: 1 VEP NON-TARGET TARGET 1 1 F3 F3 0
Page 114 and 115: and the Lorenz equations (a model o
Page 116 and 117: P300 deection. Due to the relation
Page 118 and 119: 7 General Discussion In this chapte
Page 120 and 121: Wavelet analysis was also applied t
Page 122 and 123: aect the whole spectrum and for thi
Page 124 and 125: peaks. 7.2.3 Wavelet Transform vs.
Page 126 and 127: the ones having wide peaks (being t
Page 128 and 129: A Time-frequency resolution and the
Page 130 and 131: Z 1 ;1 tx(t) d dt x(t) dt = 1 2 Z 1
Page 132 and 133: Figure 37: Time-frequency resolutio
Page 136 and 137: Berger, H. Uber das Elektrenkephalo
Page 138 and 139: Gastaut H and Broughton R. Epilepti
Page 140 and 141: Lopes da Silva FH, van Lierop THMT,
Page 142 and 143: Pradhan N, Sadasivan P, Chatterji S
Page 144 and 145: Serrano E, Ph.D. Thesis, Department
Page 146: Biographical sketch 21.03.1967 born
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