## 3 Results  
The multivariate empirical mode decomposition (MEMD) algorithm using APTIT-MEMD revealed that the number of decomposed Intrinsic Mode Functions (IMFs) was consistent across all EEG leads in each subject. However, the power spectra of the six IMFs showed variations in magnitude, with IMF3 and IMF4 exhibiting higher magnitudes. This issue poses a challenge to the visual inspection of the spectra of all IMFs in a single diagram, as depicted in Figure 2A,B. Alternatively, representing individual IMF spectra, as shown in Figure 2A,B', can be a suitable solution. Although IMF7 was not considered in this study,its mean frequency for the whole group (0.45 Hz) fell below the lower limit of the spectral range investigated (>1 Hz), and we included it in the figure for more clarity. Regardless of the graphical method used, most of the spectra of IMFs across all EEG leads displayed overlapping spectral content between adjacent IMFs. The sequence of consecutive instantaneous values of frequency and power obtained after applying Hilbert transformation to the IMFs in one of the volunteers is presented in Figure 3 for a 2000-sample segment (10 s). The method of grand averages showed that the overlapping of spectral content between IMFs, observed in Figure 4A,B for an individual, was also found for the whole group of healthy volunteers (see Figure 4B,C). The grand average of the distributions of the instantaneous frequencies of the IMFs showed, in general, a normal distribution for the EEG leads. An illustration of this fact is shown related to the results obtained for the EEG lead C3 (Figure 4A). Because of the constant properties of the discrete frequencies of the FFT method, only the spectra of the IMFs are depicted in Figure 4B,C. The approach based on the HHT also showed the presence of similar frequencies in different IMFs, but the degree of the overlap appears to be less marked than the observed FFT spectra of the IMFs for the same EEG lead. Figure 2: Power spectra of the IMFs extracted using the APIT-MEMD algorithm in a representative healthy volunteer. Panels (**A**,**B**) show the superimposed spectra corresponding to the EEG leads C3 and C4, while panels (**A**✓,**B**✓) show the same power spectra of the individual using another format that avoids the effects of the different values of the amplitude of the spectra IMFs for the visual inspection. Figure 4: Panel (**A**) shows the grand average of distributions of the instantaneous frequency for the first 6 IMFs decomposed from the EEG of 34 healthy volunteers using the APIT-MEMD algorithm and the Hilbert–Huang Transform. Panel (**B**) depicts the grand averages of the FFT spectra of the first six IMFs, while Panel (**C**) excludes the average spectrum corresponding to IMF3 to facilitate the visual inspection of IMFs with lower PSD values (IMF1 and IMF2). Results correspond to the calculations for EEG-lead C3. Figure 3: Collage showing, in the left panel, the first six IMFs decomposed from the EEG segment of a healthy volunteer using APIT –MEMD. The central panel depicts the instantaneous values of spectral frequency of the corresponding IMFs, and the right panel shows the instantaneous values of spectral power obtained using the HHT transform. The colored lines are used to differentiate the IMFs which are signaled with letters too. The mean weighted spectral frequencies (mWf) calculated with the two methods resulted in values that significantly differed. The HHT method showed significantly higher values for all the EEG leads of IMF 1 to IMF3, and lower values for IMF6. There were no found differences for the values calculated for IMF4 and only two for IMF5 (See Figure 5). Only some degree of the overlapping of spectral frequencies between IMF3 and IMF4 was detected for both methods. However, the values corresponding to the estimations of the PSD in normalized units (%) only showed isolated differences in some EEG leads (refer to Figure 6).