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

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In conclusion, our results point towards a distributed alpha system with functional relation to sensory processing and possibly to further processes. 4.6 Application to gamma responses of bisensory event-related potentials 4.6.1 Introduction During the last ten years gamma-band phenomena gained increasing popularity since the reports of Gray and Singer (1989). Measuring the spike activity of cellular units and multi-units of the cat's striate cortex they found burst activities to be in congruence with oscillatory LFPs (local eld potentials) in orientation-specic columns. Later ndings (Eckhorn et al., 1988, Gray etal. 1989) led to the proposal of a mechanism suf- cient to explain the formation of distant neuronal units to assemblies. Such assembly formation has been suggested as the brain's solution to the problem of encoding. The so-called \binding-theorem" provided a solid explanatory base in congruence with the experimental data. Although being very elegant in solving the problem to encode a quasi-unlimited number of real object features and combinations in a limited number of neural elements interest has, nevertheless, not swayed from the experimental results, which were not directly related to theoretical questions. As, for example, the studies of Tiitinen et al. (1993) and Pulvermuller et al. (1995) could demonstrate, the 40Hz oscillatory activity is not restricted to sensory processing, but can be rather modulated or triggered by cognitive processes as well. 4.6.2 Material and Methods Event-related potentials were measured in 15 healthy subjects who had neither any known neurological decit nor reported intake ofdrugsknown to aect the EEG. Electrodes were placed according to the 10 ; 20 system in F3, F4, Cz, C3, C4, T3, T4, P3, P4, O1 and O2 locations, referenced to linked earlobes. Data were amplied with a time constant of 1:5sec: andalow-pass lter at 70Hz. For each single sweep, 1sec: pre- and post-stimulus EEG were digitized with a sampling rate of 250Hz and stored in a hard disk. Subjects were instructed to view and listen passively to the stimuli. A recording session consisted of3parts: 1. Registration of 120 sweeps covering 2s time-windows with application of unimodal stimulation presenting a 2kHzsinusoidal tone-step binaurally. 61
2. Registration of 120 sweeps covering 2s time-windows with application of unimodal stimulation using a rectangular light-step centered in the visual eld at a distance of 1.5 m. 3. Registration of 120 sweeps covering 2s time-windows with application of multimodal stimulation. Stimuli of (1) and (2) were applied simultaneously. After visual inspection of the data, 30 sweeps free of artifacts were selected for each modality for future analysis. A Wavelet Transform was applied to each single sweep using a quadratic B-Spline function as mother wavelet. After a ve octave wavelet decomposition, the components of the gamma band 31 ; 62Hz were analyzed. For each subject the gamma components of the 30 single sweeps were averaged. Results for each subject were averaged obtaining a \grand average". Statistical analysis The wavelet coecients processed were the ones obtained after averaging the responses of the 30 single trials for each electrode and subject. Then, wavelet coecients were rectied and the maximum coecient was computed in a time window of 250ms post-stimulation. A multiple factor ANOVA (MANOVA) with repeated measures test was applied in order to compare dierences between modalities and electrodes. Furthermore, maximum wavelet components pre- and post-stimulation were compared with t-tests in order to analyze statistical signicance of the amplitude enhancements for each electrode and modality. 4.6.3 Results Gamma wavelet coecients of one typical subject are shown in gure 22. In central locations gamma amplitude enhancements are more pronounced upon bimodal stimulation. In this case, pre-stimulus EEG amplitude is less than 5 (in arbitrary units) and post-stimulus enhancements reach values up to 12. Further increases are seen in right frontal and right occipital electrodes, in the latter case appearing with some delay. The grand average of all the subjects is shown in gure 23. With auditory stimulation enhancements are visible in the central electrode and more poorly dened in the occipital ones. Visual stimulation shows enhancement onlyinthecentral electrode. On the other hand, bimodal stimulation evokes signicantly higher enhancements than the other two modalities (p < 0:01 see also g. 24) and they are distributed all along the surface EEG, being more pronounced in the central location. Furthermore, bimodal responses are clearly not a linear superposition of the auditory and visual ones, this fact being very clear in central electrodes and specially in the frontal ones, where enhancements occur only upon bimodal stimulation. 62
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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
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 76 and 77: Electrode F3 F4 Cz P3 P4 O1 O2 Dela
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.
Page 126 and 127: 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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