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

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17 Figure 5: Topographical mapping of the dierent EEG frequency bands from a normal EEG recording taken with eyes closed.
and Lopes da Silva, 1988 Garcia and Quian Quiroga, 1998). Figure 5 shows the topographic mapping of an EEG recording of a normal subject with eyes closed. Five dierent frequency bands are plotted. As expected, the power is homogeneously distributed in all frequency bands, except in alpha, where there is a simetrical increase in the posterior locations. This increase reects the presence of the normal spontaneous alpha activity described in section 1.1.1. 2.3.3 Frequency analysis of evoked responses Considering the proposed relation between EEG and ERP described in section x1.3, the physiological functions of the frequency bands can be analyzed by studying their response to dierent types of stimulation. Basar (1980) described a combined analysis procedure for achieving this goal. The procedure relies on the study of enhancements of dierent frequency bands as a response to stimulation. After a Fourier analysis of the pre- and post-stimulus EEG, frequency ranges are determined and bands are separated by using a selective ltering. details see Basar, 1980): I will summarize here some of these results (for more Delta enhancements were correlated with cognitive processes like signal matching or decision making by using auditory and visual oddball paradigms (Stampfer and Basar, 1985 Basar-Eroglu et al., 1992 Schurmann et al., 1995 Demiralp et al., 1999). Theta enhancements were correlated with cognitive tasks like focused attention and signal detection by using omitted stimulus paradigms in free moving cats (Basar-Eroglu et al., 1991) and in humans (Demiralp and Basar, 1992). Alpha enhancements were correlated to primary sensory processing in several experiments using cross-modality stimulation in cats and humans (Basar, 1980 Basar et al., 1992 Basar and Schurmann, 1996). Gamma enhancements were correlated with binding of features (Gray and Singer, 1989 Gray et al., 1989) and they were suggested to be involved in several other sensory and cognitive processes (Basar-Eroglu et al., 1996a,b Schurmann et al., 1997). 2.3.4 Coherence The coherence function gives a measure of the linear correlation between two EEG electrodes. The main advantage over previous \cross-correlation" measures (see Gevins, 18
Page 1 and 2: Quantitative analysis of EEG signal
Page 3 and 4: 1. Berichterstatter: Prof. Dr.-Ing.
Page 5 and 6: an okzipitalen Positionen und (c) d
Page 8 and 9: To my family: Mama, Consuelo, Hugui
Page 10 and 11: Preface In this work, I will descri
Page 12 and 13: I would certainly like to remember
Page 14 and 15: Contents Zusammenfassung Preface Ac
Page 16 and 17: 5.2.6 Calculating Lyapunov Exponent
Page 18 and 19: Summary Since the rsts recordings i
Page 20 and 21: 1 Outline of Neurophysiology: Brain
Page 22 and 23: Figure 2: Normal 20 channels (10-20
Page 24 and 25: 1.1.2 EEG in Epilepsy One important
Page 26 and 27: Figure 3: Scalp EEG of 18 channels
Page 28 and 29: Figure 4: Averaged responses upon N
Page 30 and 31: 2 Fourier Transform 2.1 Introductio
Page 32 and 33: I xx (k) =jX(k)j 2 = X(k) X (k) (
Page 36 and 37: 1987) is that coherence gives the c
Page 38 and 39: 3 Gabor Transform (Short Time Fouri
Page 40 and 41: where k g D k= R 1 ;1 jg D(t 0 )j 2
Page 42 and 43: and the band maximum peak frequency
Page 44 and 45: Figure 7: Intracraneal seizure reco
Page 46 and 47: Figure 10: Mean (soft line) and max
Page 48 and 49: EEG as the one with least amount of
Page 50 and 51: Figure 11: Scalp EEG of the right c
Page 52 and 53: 3.5 Conclusion In this chapter I de
Page 54 and 55: Original signal FOURIER TRANSFORM G
Page 56 and 57: 4.2.3 Multiresolution Analysis Cont
Page 58 and 59: Original signal -15 0 15 -1sec 0 1s
Page 60 and 61: 4.2.5 Wavelet Packets In the approa
Page 62 and 63: marked decrease in computational ti
Page 64 and 65: ain activity during an epileptic se
Page 66 and 67: 3 2 3 3 3 3.2 Hz 3.6 Hz 4.0 Hz 4.4
Page 68 and 69: Silva et al.,1973a,1973b Lopes da S
Page 70 and 71: sweep #1 sweep #2 sweep #3 sweep #4
Page 72 and 73: VEP non-target target F3 F4 F3 F4 F
Page 74 and 75: VEP non-target target F3 F4 F3 F4 F
Page 76 and 77: Electrode F3 F4 Cz P3 P4 O1 O2 Dela
Page 78 and 79: In conclusion, our results point to
Page 80 and 81: 63 Figure 22: Gamma responses for a
Page 82 and 83: 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
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the sum of the positive exponents (
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performed. On one hand, must be lar
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Duke (1992) and Elbert et al. (1994
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agation time. They applied this pro
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Figure 28: Histogram for the EEG da
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Figure 29: Attractors corresponding
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able for automatic detection proces
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ENTROPY Thermodynamics Signal analy
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6.3 Application to visual event-rel
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VEP Non Target Target F3 -20 -10 0
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1 VEP NON-TARGET TARGET 1 1 F3 F3 0
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1 VEP NON-TARGET TARGET 1 1 F3 F3 0
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and the Lorenz equations (a model o
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P300 deection. Due to the relation
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7 General Discussion In this chapte
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Wavelet analysis was also applied t
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aect the whole spectrum and for thi
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peaks. 7.2.3 Wavelet Transform vs.
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the ones having wide peaks (being t
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A Time-frequency resolution and the
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Z 1 ;1 tx(t) d dt x(t) dt = 1 2 Z 1
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Figure 37: Time-frequency resolutio
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References Abarbanel HDI, Brown R,
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Berger, H. Uber das Elektrenkephalo
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Gastaut H and Broughton R. Epilepti
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Lopes da Silva FH, van Lierop THMT,
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Pradhan N, Sadasivan P, Chatterji S
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Serrano E, Ph.D. Thesis, Department
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Biographical sketch 21.03.1967 born
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