###### Abstract  
This study investigates the use of empirical mode decomposition (EMD) to extract intrinsic mode functions (IMFs) for the spectral analysis of EEG signals in healthy individuals and its possible biological interpretations. Unlike traditional EEG analysis, this approach does not require the establishment of arbitrary band limits. The study uses a multivariate EMD algorithm (APIT-MEMD) to extract IMFs from the EEG signals of 34 healthy volunteers. The first six IMFs are analyzed using two different methods, based on FFT and HHT, and the results compared using the ANOVA test and the Bland-Altman method for agreement test. The outcomes show that the frequency values of the first six IMFs fall within the range of classic EEG bands (1.72-52.4 Hz). Although there was a lack of agreement in the mean weighted frequency values of the first three IMFs between the two methods (>3 Hz), both methods showed similar results for power spectral density (<5% normalized units, %, of power spectral density). The HHT method is found to have better frequency resolution than APT-MEMD associated with FFT that produce less overlapping between IMF3 and 4 (\(p\) = 0.0046) and it is recommended for analyzing the spectral properties of IMFs. The study concludes that the HHT method could help to avoid the assumption of strict frequency band limits, and that the potential impact of EEG physiological phenomenon on mode-mixing interpretation, particularly for the alpha and theta ranges, must be considered in future research. empirical mode decomposition; Hilbert transform; Hilbert-Huang transform; EEG; non-stationary analysis 20232023203  
measured categories of ultra-fast EEG signals in the 500-1000 Hz range are brain stem evoked potentials. The study of the sources of these oscillations in different cortical and subcortical brain structures has evolved in parallel with the study of EEG frequency bands and rhythms [9; 10; 11; 12]. Various studies have shown that when frequency bands are arranged in increasing order of their center frequencies, some degree of overlap can be observed between adjacent bands' oscillations and that the natural logarithm of these frequencies forms a linearly increasing continuum [9; 10; 11; 12; 13]. A key tool for studying brain rhythms was the development of the FFT algorithm [14], which was incorporated into EEG spectral analysis and continues to be used today with several improvements. The dynamics of EEG have nonlinear and non-stationary properties [15; 16; 17; 18], which means that traditional FFT methods may not be suitable for analyzing this type of data and may result in misinterpretation of results obtained through FFT. An innovative data-driven technique called empirical mode decomposition (EMD) has been introduced to analyze non-Gaussian, nonlinear, and non-stationary signals [19]. When combined with the Hilbert transform of the EMD-extracted modal components, this method is also known as the Hilbert-Huang transform (HHT) method and has been applied in the analysis of EEG signals [5; 15; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. As feature extraction of the EEG is improved through HHT, applications for other fields, such as brain-computer interface and machine learning, have arisen as a natural consequence [36; 37; 38; 39]; however, less attention has been paid to the biological meaning of the IMFs themselves or how the mode-mixing problem affects their interpretation in terms of physiology. In recent years, the study of the spectral frequency and power content of EEG oscillatory modes extracted through EMD has been applied for various purposes. Despite the extensive use of EMD for spectral analysis of EEG signals, there remains a lack of comprehensive studies focusing on healthy individuals and the biological interpretations of the extracted intrinsic mode functions (IMFs). Most existing studies have utilized clinical databases or have involved only a limited number of participants and EEG leads.