**3.2. EEG signal analysis**

#### *3.2.1. Spectral analysis*

Spectral analysis of EEG signal is based on the fast Fourier transformation (FFT), which decomposes the signal according to the mean amplitude of each frequency in the signal. By applying second-order FFT, the result is the spectral power graphic, which decomposes the signal based on amplitude squared/frequency (microvolts<sup>2</sup> /Hz). Analyzing this graphic provides very important parameters to estimate the depth of sedation/anesthesia.

Median frequency (MEF) represents the value of frequency whose perpendicular meets Ox in the point that splits equally the area under the spectral power graphic.

Spectral edge frequency (SEF) is the value of frequency from which we can draw a perpendicular to Ox that leaves 90 or 95% of the spectral power function under graphic area to the left [20] (**Figure 4**).

If the patient is anesthetized, the values of these parameters will decrease proportionally with the degree of sedation (they will shift to the left), because during sedation, the high-frequency fast waves EEG activity ceases [21]. Surgical anesthesia is performed at the moment the EEG shows mostly theta waves.
