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

More documents

Recommendations

Info

sweep #1 sweep #2 sweep #3 sweep #4 sweep #5 -50 -50 -50 -50 -50 Original sweep 0 50 0 50 0 50 0 50 0 50 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 53 Digital filtering -50 -50 0 0 -50 -50 -50 0 0 0 50 50 50 50 50 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -150 -150 -150 -150 -150 Wavelet coeff. 0 0 0 0 0 150 150 150 150 150 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -50 -50 -50 -50 -50 Wavelet reconstr. 0 50 0 50 0 50 0 50 0 50 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 Figure 18: Examples of the better performance of the Wavelet Transform in comparison with an \ideal" digital ltering for ve single sweeps.
vertical lines, is showed a transient with a frequency clearly lower than the range of alpha band. The digital ltering does not resolve this transient and it spuriously \interpolates" alpha oscillations in continuity with the ones that precede or follow the transient. On the other hand, the wavelet coecients show a decrease in this time segment, this phenomenon being also visible in the reconstructed form. Something similar occurs in sweep #3 with the transientmarked with an arrow. In fact in this last case, the transient is due to the cognitive P300wave typically obtained upon target stimuli. With wavelets it is visible that, as in the original signal, there is no important contribution of alpha oscillations in this time range, the digital ltering having not enough resolution for resolving this. The better time-frequency resolution of wavelets (in this case a better frequency localization for a certain time range) can be also seen in sweep #4. In the original signal, in between the vertical dashed lines there is a marked oscillation of about 4 ; 6Hz, corresponding to the theta band. The digital ltered signal shows an alpha oscillation not present in the original signal. On the other hand, due to its better resolution the wavelet coecients and the reconstructed signal show a clear decrease for this time range. Finally, sweep #5 shows a ringing eect (i.e. spurious oscillations appearing before the stimulation time point due to time resolution limitations). The oscillation before the stimulation time, marked with an arrow, appears in the digital ltered signal with more amplitude than in the original signal, this eect being overcome with wavelets. 4.5.3 Results The grand average wideband ltered (0:5 ; 100Hz) event-related potentials are shown in gure 19. The P100 response is clearly visible upon all stimuli types at about 100ms, best dened in occipital locations, where it reaches amplitudes about 8V . In the case of target stimulation, a marked positive peakappears between 400 ; 500ms, according to the expected cognitive (P300) response (see sec. x x1.2). Figure 20 shows the wavelet components in the alpha band (for brevity, \alpha responses") for the subject JA (for a better visualization of the responses, the signal reconstructed from the alpha band wavelet coecients is shown). One second pre- and one second post-stimulation EEG are plotted. Alpha components corresponding to the pre-stimulus EEG have about 5V and upon all stimulus types, amplitude enhancements are clearly marked in posterior locations reaching values up to 20V . Furthermore, in posterior electrodes responses upon TARGET stimulation are prolonged compared with the other two stimulus types. Results for the grand average of the 10 subjects (g. 21) are similar to the ones outlined for the rst subject. Amplitude increases were distributed over the whole scalp 54
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 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 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
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 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
show all

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

Create successful ePaper yourself

Delete template?

Save as template?