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

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<strong>and</strong> the b<strong>and</strong> maximum peak <strong>frequency</strong> (f (i)<br />

M (t)) <strong>of</strong> the i-b<strong>and</strong>, as the <strong>frequency</strong> value for<br />

which I(f t) is maximum in the interval ( f (i) min f (i) max )ata time t, i.e.<br />

I(f M t) >I(f t)<br />

8 f 6= f M (f (i)<br />

min f(i) max) (25)<br />

Uncertainty Principle<br />

One critical limitation appears when windowing the data due to the Uncertainty<br />

Principle (Cohen, 1995 Chui, 1992 Qian <strong>and</strong> Chen, 1996 Kaiser, 1994). If the window<br />

is too narrow, the <strong>frequency</strong> resolution will be poor, <strong>and</strong> if the window is to wide, the<br />

time localization will be not so precise. Or in another words, sharp localizations in<br />

time <strong>and</strong> <strong>frequency</strong> are mutually exclusive because a <strong>frequency</strong> can not be calculated<br />

instantaneously. If we denote by t the time uncertainty (time duration) <strong>and</strong> by f the<br />

uncertainty in the frequencies (<strong>frequency</strong> b<strong>and</strong>width), in the case <strong>of</strong> normalized <strong>signals</strong><br />

the Uncertainty Principle can be expressed as follows (see appendix xA for a pro<strong>of</strong>):<br />

t f 1<br />

(26)<br />

4<br />

This limitation becomes important when the signal has transient components localized<br />

in time as in the case <strong>of</strong> <strong>EEG</strong>s or ERPs. Gabor (1946) suggested a Gaussian<br />

function as the smoothing function, owing to its good localization in time <strong>and</strong> <strong>frequency</strong>.<br />

In fact, with a Gaussian function eq. 64 is an equality <strong>and</strong> furthermore, the equality also<br />

holds for the mother function <strong>of</strong> the Gabor Transform, g D e i2ft (see appendix xA).<br />

3.3 Application to intracranially recorded tonic-clonic seizures<br />

3.3.1 Methods <strong>and</strong> Materials<br />

Seizure <strong>EEG</strong> recordings were obtained from a 21 years old male patient. A nine hours<br />

recording was performed with 12 depth electrodes ( each electrode having 5 to 15 contacts<br />

) placed in the epileptogenic zone <strong>and</strong> propagating brain areas. Each signal was<br />

amplied <strong>and</strong> ltered using a 1;40Hz b<strong>and</strong>-pass lter. A 4 pole Butterworth lter was<br />

used as low-pass lter <strong>and</strong> as an anti-aliasing scheme. After 10 bits A/D conversion the<br />

<strong>EEG</strong> data was written continuously onto a hard drive with a sampling rate <strong>of</strong> 256Hz<br />

per channel. Selected data sets <strong>of</strong> ictal <strong>and</strong> interictal activity were stored for subsequent<br />

o-line <strong>analysis</strong>. We established the necessary <strong>frequency</strong> resolution in f = 0:25Hz<br />

in order to have enough <strong>frequency</strong> values for calculating the mean <strong>and</strong> maximum b<strong>and</strong><br />

frequencies <strong>and</strong> the time resolution was set to t = 0:25sec. This was done by using<br />

a Gaussian window <strong>of</strong> D = 4sec width 2 with slide displacement steps <strong>of</strong> 0:25sec. The<br />

2 from eq. 8, f = 1 = 1<br />

= 1 Hz =0:25Hz<br />

N 4256Hz(1=256Hz) 4<br />

25

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