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

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Electrode F3 F4 Cz P3 P4 O1 O2 Delay 246.03 290.86 248.20 209.20 209.27 172.37 181.03 SEM 21.09 23.35 22.76 20.51 22.10 18.42 17.45 F3 XXX - - - - < 0.05 < 0.05 F4 XXX - < 0.01 < 0.01 < 0.01 < 0.01 Cz XXX - - < 0.05 < 0.05 P3 XXX - - - P4 XXX - - O1 XXX - O2 XXX Table 3: Multiple factor ANOVA comparison of the time delays of the maximum alpha band wavelet components for the factor electrode. Note: SEM means standard error of the mean, - means no signicance, delays in ms. 4.5.4 Discussion Resonance theory In the present work, occipital maximum enhancements were located between 100 and 200ms, strongly stressing the importance of the alpha band in the generation of the P- 100 peak and the following N-200 negative rebound. This is in agreement with the results of Stampfer and Basar (1985), who, by digitally ltering auditory evoked potentials in humans, showed that sensory evoked responses were generated and shaped mostly by alpha and theta oscillations. This result is directly related with the resonance theory. In principle, if the alpha enhancements were found only at the time of appearance of the P100-N200 peaks, it can be argued that they were just a result of the morphology of these peaks. However, the prolonged alpha response observed in some of the subjects showed that this is not the case, and that it is more plausible to think about enhanced spontaneous oscillations as a response to the stimulus, according to the resonance theory presented in section x1.3. Functional correlates of event-related alpha oscillations Post-stimulus amplitude increases of alpha band were observed in all electrodes, being signicantly higher in the occipital ones. Furthermore, anterior responses were delayed in comparison with the posterior ones. Frontal maximum responses appeared between 50 and 100ms after the occipital ones, this delay being statistically signicant. Consequently, a primary response was reached at occipital locations, later propagating to parietal, central and frontal electrodes. Then, owing to the anatomy of the visual 59
pathway (Mason and Kandel, 1991 Shepherd, 1988), the occipital localization and the short latency of this response points towards a relation between event-related alpha oscillations and the rst stages of sensory processing. Moreover, the idea of sensory processing is reinforced by the independence of the response from the type of stimulus used. Since the 1940s, the event-related alpha response was interpreted as the reactiveness of the brain to sensory stimuli. A sensory alpha response was also observed in several intracortical structures of the cat brain upon auditory and visual stimulation (for a review of these works see Basar et al., 1997). However, this process should be not considered as the only one related with the alpha band. For example, in some subjects alpha responses were prolonged upon TARGET stimuli in posterior locations. Spontaneous alpha activity, low amplitude long-latency responses and low phase locking could be responsible of the lack of statistical signicance when considering the whole group of subjects. Consequently, it could be conjectured that other processes related with a cognitive function, due to the long latency and the appearance only upon target stimuli, were present in some of the subjects. The cognitive function of alpha band was already pointed out in previous works (Stampfer and Basar, 1985 Kolev and Schurmann, 1992 Basar et al., 1992 Klimesch, 1997 Maltseva and Masloboev, 1997 and Petsche et al., 1997). Further experiments applying dierent types of tasks should be performed in order to learn more about cognitive related alpha responses. Sources of event-related alpha oscillations Another interesting question about alpha rhythms deals with its sources of generation. Alpha responses were best visible in occipital electrodes, but enhancements were also present in other locations with some time delay. Owing to the high dierences in the delays between occipital and anterior electrodes, the conjecture of a single generator and dispersion by volume conduction must be ruled out. A more plausible idea is to think about several generators activated at dierent times and with dierent strength depending on the stimuli and paradigm used. Several previous ndings support a multiple alpha generator theory. Alpha rhythms were described mainly in the thalamus and cortex (Adrian, 1941 Andersen and Andersson, 1968 Lopes da Silva and Storm van Leewen, 1977), but alpha activity was also found in other structures like the brain stem, cerebellum and limbic system, (for a review see Basar et al., 1997). Further evidence showing that alpha rhythms cannot be explained through generators only in the thalamus or cortex are the experiments performed on the cerebral ganglion of aplysia and with the isolated ganglia of Helix pomata (Schutt et al., 1992 Schutt and Basar, 1992). These studies provided evidence that 10Hz activity can be recorded in vitro in such small neural populations, each consisting only of approximately 2000 neurons. 60
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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
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 34 and 35: 17 Figure 5: Topographical mapping
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 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
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.
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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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