## 5 Conclusions  
In this study, the Multivariate Empirical Mode Decomposition (APIT-MEMD) algorithm was used to decompose EEG signals recorded simultaneously from different locations on the scalp of healthy human subjects. This method provided distinct multi-component oscillatory modes whose frequency ranges were associated with those traditionally assigned to spectral bands in classical electroencephalography. With the exception of frequency estimation in the first two IMFs, which contained information about faster EEG activity, the information obtained using the FFT was found to be as informative as that obtained using the Hilbert-Huang transform (HHT). Despite this similarity, HHT is recommended due to superior frequency resolution and lower overlap percentage.  
Our study also observed the phenomenon of overlapping frequencies in the third and fourth oscillatory modes, a problem often reported by others authors and related to mode-mixing in EMD algorithms. However, this does not exclude the possibility that the observed frequency overlap between oscillatory modes may be due to the presence of multiple oscillatory patterns simultaneously modulating each other in the EEG. Given the frequency range and topography of the overlap, it appears that alpha rhythm variability may be related to it.  
Overall, this novel approach to the study of EEG rhythms holds promise as a viable alternative to the FFT method, especially given the presence of non-Gaussian, non-linear, and non-stationary processes in EEG signals, which can lead to misleading results when using the FFT approach because it is strictly valid only for linear and stationary processes. The HHT method could also help to avoid the assumption of strict frequency band limits, but the potential impact of EEG physiological phenomena on the interpretation of mode mixing, especially for the alpha and theta ranges, needs to be considered in future research.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/signals4030026/s1](https://www.mdpi.com/article/10.3390/signals4030026/s1)]([https://www.mdpi.com/article/10.3390/signals4030026/s1](https://www.mdpi.com/article/10.3390/signals4030026/s1)), Table S1: Mean weighted frequency values (Hz) calculated from the intrinsic mode functions extracted from the EEG of thirty-four healthy volunteers using two spectral methods; Table S2: Power spectral density expressed in normalized units (%) calculated from the intrinsic mode functions extracted from the EEG of thirty-four healthy volunteers using two spectral methods.  
E.A.-P.: Conceptualization; data curation; formal analysis; investigation; methodology; project administration; software; resources; supervision; writing--original draft; writing--review and editing. M.E.-B.: Conceptualization; data curation; formal analysis; investigation; methodology; software; supervision; validation; visualization; writing--original draft; writing--review and editing. J.M.E.-C.: Data curation; formal analysis; investigation; methodology; validation; visualization. G.L.: Writing--review and editing; formal analysis; data curation. C.M.: Data curation; investigation; resources; supervision. C.B.-L.: Data curation; project administration; resources; supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We regret to inform that due to institutional privacy and ethical restrictions, the raw data generated or analyzed during the course of our study is not publicly available. However, tables with the values used for the graphical representation and analysis can be checked in the supplementary material.  
We are grateful for [PERSON]'s invaluable guidance, mentorship, and support throughout our academic journey. His knowledge and expertise in the field have shaped our research interests and provided an inspiration in the most difficult moments. Thank you for your encouragement and generosity.  
The authors declare no conflict of interest.