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

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Figure 11: Scalp EEG of the right central channel during a Tonic-Clonic seizure Figure 12: RIR corresponding to the previous seizure 33
these factors. A rise in delta activity was observed towards the end of the seizures. This pattern was also seen with depth recordings, thus making unlikely the possibility of being generated by artifacts. Neuronal fatigue, decrement in neuronal ring and preponderance of inhibitory mechanisms are critical factors in the mechanisms underlying this observation which prompts further research. These results were obtained by avoiding muscle artifacts with Gabor Transform, thus supporting their utility for the evaluation of tonic-clonic seizures, something dicult to assess in scalp recorded EEGs. As Niedermeyer (1993c) pointed out: \Muscle activity rapidly obscures the recording the vertex deviation, however, may remain artifact-free due to the lack of underlying muscles. Informative Grand Mal recordings can be secured only from patients with muscle relaxation from curarization 4 and articial respiration". Gotman et al. (1981) avoid this problem by the use of lters. However, he pointed out that ltering the signal has several disadvantages. On the one hand it is impossible to separate brain and muscle activity in the EEG, and further, it is well known that ltering high frequencies also aects the morphology of the low ones. Gastaut and Broughton (1972) instead, described a frequency pattern during a tonic-clonic seizure of a curarizated patient. In the rst seconds after the seizure onset, they found an epileptic \recruiting rhythm" of 10Hz associated with the tonic phase that lasted approximately 10 seconds later, as the seizure ended, there was a progressive increase of the lower frequencies (5 ; 6Hz) associated with the clonic phase. Our ndings were similar to the ones highlighted by Gastaut and Broughton, but recruiting rhythms were observed to be also in the theta range or, as seen in most cases, uctuating between alpha and theta. Darcey and Williamson (1985) also obtained similar patterns by analyzing seizures recorded with depth electrodes. They reported an activity characterized by 10Hz at the onset of the seizure, frequency that declined as the seizure ended. The results presented here diers from these previous attempts in that a quantication of the Gabor Transform was performed with the RIR, thus allowing the analysis of the frequency content of the seizure. One important point to note is that these results were obtained with scalp recordings and without the need for curare or any ltering method. 4 Curare is a drug that blocks neuromuscular transmission, thus inhibiting the muscular artifacts characteristic of the Grand Mal seizures 34
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 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 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
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
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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
Page 116 and 117:
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
Page 128 and 129:
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,
Page 136 and 137:
Berger, H. Uber das Elektrenkephalo
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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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