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

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Lopes da Silva FH, van Lierop THMT, Schrijer CF, Storm van Leeuwen W. Organization of thalamic and cortical alpha rhythms: spectra and coherences. Electr. Clin. Neurophysiol., 1973a 35: 626-639. Lopes da Silva FH, van Lierop THMT, Schrijer CF, Storm van Leeuwen W. Essential dierences between alpha rhythms and barbiturate spindles: spectra and thalamo-cortical coherences. Electr. Clin. Neurophysiol., 1973b 35: 641-645. Lopes da Silva FH, Storm van Leeuwen W. The cortical source of the alpha rhythm. Neurosci. Lett., 1977 6: 237-241. Lopes da Silva FH. Neural mechanisms underlying brain waves: From neural membranes to networks. Electr. Clin. Neurophysiol 1991, 79: 81-93. Lopes da Silva F H. EEG analysis: theory and practice. In: E. Niedermeyer and F. H. Lopes da Silva (Eds.), Electroencephalography: Basic Principles, Clinical Applications and Related Fields. Baltimore: Williams and Wilkins 3rd ed., 1993a, pp: 1097-1123. Lopes da Silva F H. Computer-assisted EEG diagnosis: Pattern recognition and brain mapping. In: E. Niedermeyer and F. H. Lopes da Silva (Eds.), Electroencephalography: Basic Principles, Clinical Applications and Related Fields. Baltimore: Williams and Wilkins 3rd ed., 1993b, pp: 1063-1086. Lorenz E. Atmospheric predictability as revealed by naturally occurring analogies. J. Atmos. Sci., 1969 26: 636. Lutzenberger W, Preissl H and Pulvermuller F. Fractal dimension of electroencephalographic time series and underlying brain processes. Biol. Cybern., 1995 73: 477-482. Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Analysis and Machine Intell., 1989 2 (7): 674-693. Maltseva IV and Masloboev YP. Alpha rhythm parameters and short-term memory span. In Brain Alpha Activity - New aspects and Functional Correlates. EBasar, R Hari, FH Lopes da Silva and M Schurmann (eds.). Int. J. Psychophysiology, special issue, 1997 26: 369-380. Mason C and Kandel E. Central visual pathways. In E. Kandel, J. Schwartz and T. Jessell (Eds.) Principles of neural science. Elsevier, New York 1991, pp: 420-439. Maurer K and Dierks T. Atlas of brain mapping. Springer-Verlag, Berlin 1991. Mees, A., Rapp, P. and Jennings, L. Phys. Rev. A 36, 340 1987. Meierkord H, Will B, Fish D, Shovone S. The clinical features and prognosis of pseudoseizures diagnosed using video EEG telemetry. Neurology 1991 41: 1643-1646. Molle M, Marshall L, Lutzenberger W, Pietrowsky R, Fehm H and Born J. Enhanced dynamic complexity in the human EEG during creative thinking. Neurosc. Lett., 1996 208: 61-64. Mpitsos, GJ. Chaos in Brain Function and the Problem of Nonstationarity: A Commentary. In: E. Basar and T.H. Bullock (Eds.) Brain Dynamics. Springer-Verlag, Berlin, 1989, pp: 521-535. Niedermeyer E. The normal EEG of the waking adult. In: E. Niedermeyer and F.H. Lopes da Silva (eds). Electroencephalography: Basic Principles, Clinical Applications and Related Fields. Baltimore: Williams and Wilkins 3rd ed., 1993a, pp: 131-152. 123
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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
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wavelet coecients allowed an easy d
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the position vs. the linear momentu
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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 134 and 135: References Abarbanel HDI, Brown R,
Page 136 and 137: Berger, H. Uber das Elektrenkephalo
Page 138 and 139: Gastaut H and Broughton R. Epilepti
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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