Present results showed that the well described pattern of decrease in EEG power with age would be organized in six different frequency ranges. The correlation pattern of different frequencies suggests a certain asynchrony in the maturation, which would be a consequence of the six frequency ranges with different EEG power ratios between children and adults in the peri-adolescent period. The narrow-band frequency correlation with age analysis corroborated the previously described pattern of an earlier maturation of posterior regions with respect to anterior regions. But low frequencies present a lower rate of maturation in the peri-adolescent period than alpha and beta rhythms when criteria based in mean comparisons and correlations comparisons are used. However, the delta-theta/high frequencies ratios were decreasing with age. The principal component analysis allowed extracting the following basic brain rhythms in children and adults with very similar topographies: beta, theta (and anterior delta), high alpha (occipito-temporal), occipital alpha, low alpha (parieto-occipital), low delta and the mu rhythm. The scalp alpha rhythm and all the extracted alpha sub-components and the extracted mu rhythm peaked at a higher frequency in young adults with respect to children. Given the different pattern of age differences when absolute and relative power are compared, some caution must be taken when extrapolating conclusions from one to the other type of analysis.
EEG power differences around the adolescence period
Children showed greater absolute spectral power than young adults in the four standard frequency bands. Spectral power decrease during maturation is a general finding [2–4, 20, 21, 32, 56]. This decrease would be attributed to a decrease in the number of cortical synapses due to synaptic pruning [14, 43, 44]. In addition, the reported metabolic decrease with the age obtained with PET extends throughout adolescence, and may be associated with this synaptic pruning . The possibility that a part of this decrease would be due to a lower electrical resistance of pericranial tissues (meninges and skull) in children compared to adults cannot be ruled out. However, magnetoencephalography (MEG) recordings have shown that also in MEG theta activity has greater amplitude in children than in adults . Given that the skull has a high magnetic permeability, the MEG decrease in theta with age must necessarily be attributed to a current decrease in brain generators during maturation. Hagemann et al.  have shown, in adults, a minor contribution of skull thickness variability to the alpha EEG power variability. Therefore, at least in part, increased EEG amplitude in children is probably due to brain rhythms generated in more intense amplitude than in young adults, probably due to an increased number of synapses in children with respect to young adults.
The obtained differences in spectral power in present report were higher in the delta and theta rhythms, as it has been previously described [3, 58]. Looking at the obtained data more carefully, when the ratios of mean and variance EEG power and the correlations with age of EEG power are considered, six different frequency ranges with a different rate of maturation are obtained. Those ranges correspond to low delta, delta-theta, low alpha, high alpha, low-beta and high-beta. It implicates that the rate of EEG power maturation is different for different frequency ranges at the peri-adolescent period. Ideally, this result would be able to provide an order of maturation for frequencies in the 0–20 Hz range. However, it is not possible to assume that EEG maturation is complete in this group of young adults, and for instance, the apparent higher maturation in the high alpha range with respect to the other frequencies would be optionally due to a lack of maturation in the young adults, instead of a complete maturation in adolescents. A complete answer to this question would need the establishment of an age complete sequence of EEG maturation covering the third decade of life.
However, in the case that EEG power maturation is already almost completed in our 18–23 years old sample, the following order of maturation for EEG absolute power frequencies can be proposed: high alpha, followed by low alpha and high beta, the low beta and low delta and finally the delta-theta range. This possible order of maturation is different to the general view of low frequencies maturing earlier than high frequencies [2, 4]. A similar decrease of delta until young adulthood has been recently observed . Part of this controversy must be due to the fact that the arguments about increasing of high frequencies with age refer to relative power or to fractions [4, 21], but also to different ages in different studies [21, 32]. The relative power has the problem of an artificial increase of high frequencies due to the dramatic decrease of lower frequencies. For this reason, the fractional method seems to be more suited to address the problem of the contributions of each frequency band to the whole spectrum. Looking at the absolute decrease or increase of absolute EEG power, it should be considered the major descriptor of EEG maturation. In fact, in present results, the delta-theta/high frequencies fractions were statistically significantly higher in children than in young adults. The latter result is due to a higher decrease of delta-theta than high frequencies with age, corroborating the higher rate of maturation of delta-theta during adolescence with respect to alpha and beta. A similar result has been recently obtained by Lüchinger et al.  for the 0–20 Hz frequency range, although they found a continuing maturation of beta in frequencies higher than the 20 Hz limit used in the present study. The present proposal of maturation order, around the adolescent period, is based on the comparison of the amplitudes of the different frequency ranges, and it can be distorted, particularly in alpha, by the fact of a different frequency for the same rhythm at different ages. The extraction of PCA components would help in solving if there is an earlier maturation of alpha with respect to delta-theta.
Interestingly, when the cross-frequencies EEG power correlations are obtained, the young adults obtained a higher pattern of correlation than the children. The latter result and the previously described six frequency ranges differential maturation indicate that there is possibly a certain asynchrony in the maturation of the different rhythms. Somsen et al.  showed differences in maturational rate of different rhythms in children between 6–11 years old. Also an asynchrony pattern of EEG development between different areas of the brain was noticed by  in late childhood. The developmental trajectories of non-REM delta and theta during sleep have also shown different developmental trajectories: delta presenting a plateau until 10 years old and then decay, while theta presents a continuous decay . Gasser et al.  also noticed a decrease in absolute EEG power with age (6–17 years old) except for high alpha, but the dynamics of EEG decrease in most bands were not parallel. The fact that in young adults the correlations are relative high suggests that, in this group of age, the dominant factor is the individual variability in EEG power, which affects to 0–20 Hz EEG frequencies. Another interesting point arising from correlation matrices is that absolute power frequencies are highly correlated with close frequencies. This is probably due to what is already seen in power spectra, where there are smooth transitions between spectral powers of different frequencies.
Mention apart requires the obtained result that the EEG power maturation in the range of very low delta (range 0 to 0.51 Hz) seems to be different than in delta-theta frequency range (.51-7.65 Hz). However, it must be kept in mind that this frequency is within the range of the high pass filter located at 0.1 Hz and 6db per octave frequency cutoff. Nevertheless, records in young adults and children received the same type of filtering, and therefore, it is difficult to assume that this difference is a consequence of signal processing. Tenke and Kayser  found a 0.8 Hz component with an anterior topography that they considered eye movements’ artifact. However, the low delta component described in present report has a lower frequency, it is posterior and our recordings were artifact-corrected. For these reasons, it is highly improbable that the low delta component would be considered exclusively an eye-movements artifact in present report. Pfurtscheller  described a 0.1 Hz brain rhythm. This low delta rhythm demonstrated to modulate the amplitude of the alpha and delta rhythm . The differential maturation rate of the low delta rhythm obtained in present report points out to an independent low delta rhythm from a faster delta rhythm, more associated to the maturation of theta. However, at this point, it is necessary to be cautious and new records and analysis are needed, particularly given the technical difficulty of obtaining a true DC recording.
The topographical pattern of the correlation of PS with age shows that, in general, correlation is higher in anterior than in posterior sites for the frequencies considered in present report, suggesting that maturation is progressing from posterior to anterior sites. The correlation with age pattern is quite similar to the mean PS comparison between age groups. In this sense, the correlation with age pattern can be considered as a measure of the size effect of mean comparisons. The present results extend to a narrowband PS analysis, previous results on broad band showing that maturation in general progresses from posterior to anterior sites. Hudspeth and Pribram  observed that the latest EEG maturation of EEG power occurred in frontal sites. These results would be coherent with the antero-posterior maturational rate obtained in present report. Otero  indicates that for theta and alpha rhythms, and to a lesser extent delta, maturation begins in posterior regions and ends in anterior regions. For beta, maturation progresses from central to lateral and finally to frontal regions. Our results suggest that the anterior-posterior rhythms pattern of maturation is general for all the frequencies considered, but with certain patterns of regional differences. This antero-posterior pattern of maturation would be related to progressive increase of frontally related cognitive functions. In this sense, several studies have shown that EEG and cognitive maturation are intimately associated [7, 29]. Hudspeth and Pribram  observed that EEG maturation progresses from posterior to frontal regions, possibly permitting the increase of more complex cognitive functions as age progresses. Recently, it has been consistently proved that brain maturation development shows progressive (axon myelinization) and regressive (synaptic pruning) phenomena [37, 38]. More specifically, grey matter decreases with age possibly due to synaptic pruning [34, 39], while white matter increases with age probably due to axon myelinization and/or axon diameter increase [1, 40]. This pattern of inverse trajectories between grey and white matter continues until 20 years old . An interesting result, which is possibly underlying the late PS maturation of anterior sites obtained in present report and others, is that frontal lobes seem to lose grey matter at a later period than posterior locations, so they are the last to mature [14, 42].
In this section of differences in PS between children and young adults, the different pattern of maturation when absolute and normalized power is used must be remarked. The normalized PS measures have been used to minimize the impact of the strong individual component in PS amplitude values and to increase test-retest reliability of EEG measurements , but this normalization procedure could produce some direct questions about their direct extrapolation to the values obtained in the absolute PS. Particularly a high interdependency between these measures can be obtained as a by-product of the normalization procedure .
The results obtained in present report show the characteristic higher normalized PS of low frequencies in children over young adults, and the higher PS in beta of young adults with respect to children. Correlation with age showed a similar pattern. This result has been classically obtained, showing the typical movement from slow to fast waves with age [2–4, 45], indicating that the normalized power is more sensitive to changes in composition of the frequencies with age than absolute frequencies . However, some topographical differences are obtained when statistically comparing the mean, variance and correlation with age between absolute and normalized power. Particularly, differences are more accused in anterior beta in absolute power, while differences are more marked in posterior beta when normalized power is used. Gasser et al.  also found an increased relative power in centro-parietal (beta) and parieto-occipital (high alpha) with age. This different result in normalized (relative) power with respect to absolute power would be a consequence of the normalization transformation, and suggests some caution when absolute and normalized power are considered.
Principal component analysis
The purpose of using a PCA analysis would be to find different sub-components in the brain rhythms which are not obvious in the scalp EEG, and demonstrate if they show some maturational trends. In this approach, we used Varimax rotated and non-rotated approaches. Both approaches are mathematically valid, the rotated Varimax approach tries to accommodate the data variance in a few components in order to simplify the physiological interpretation, and for this reason, we have chosen this approach for the interpretation of the results, except for the results obtained in the non-rotated PCA in the first component and in the third component.
The loading factors of the first component of non-rotated PCA were very high and the component scores were correlated with age. This first component of non-rotated PCA could be related to the "general alert" factor described by Lazarev  in adults and in children by . Wackermann and Matousek  described a non-linear age factor which accounted for most of the EEG power variance. In a previously reported analysis , applied PCA to the absolute broad-band PS showed that the first component is strongly associated with the average energy or average power spectrum. Therefore, this first component could be associated to the individual variability EEG power which is therefore decreasing with age. The second component presented the interesting characteristic that the loading factors were inverted in sign with the delta and the alpha band, suggesting a partial opposite inter-dependency from a single factor of the maturation of these two bands. The latter result was also previously described in the broad band analysis of this data .
The pattern of loading factors in rotated components, in terms of loading factors vs. frequency and topographies of component scores, would allow to characterize the physiological meaning of different components and to prove if children and young adults present similar components. All components obtained in children presented an homologous component in young adults, although the order of components in children and young adults obtained by the amount of explained variance was different. These homologies between children and young adults, which are also present in similar PS topographies, implicate that the structure of the EEG is already present in children in the pre-adolescent period. Different components were extracted corresponding to beta-EMG, delta-theta, a temporo-occipital high alpha, occipital alpha, parieto-occipital low alpha, low delta and mu rhythm. Beta rhythm, given the extension to lateral sites would have an important contamination from Electromyography (EMG) in these lateral sites. These extracted rhythms broadly correspond to the frequency ranges which present a certain asynchrony in the maturational pattern.
It is remarkable that peaks of loading factors in children occur at lower frequencies than adults in most cases, and this is particularly clear in the three different sub-components of alpha and in the mu rhythm. The topographic representation of PS and component scores comparing homologous components between two age groups showed very similar topographies, despite the fact that the same frequencies were not represented for the PS of children and young adults.
When loading factors of the different alpha range sub-components of children and young adults are represented, a clear same displacement from lower to higher frequencies occurs from children to young adults. The scalp frequency shift to high frequencies during maturation has been proposed to be due to an increase in the contribution of high alpha to the alpha peak . On the other hand, the presence of several components associated to alpha has been previously described by Tenke and Kayser  using power EEG, log power and amplitude spectra for extracting the PCA, and would represent different independent generators in alpha range. Therefore, the four alpha sub-components replicate the increase in frequency by maturation, which was also obtained in the scalp alpha rhythm in present report (Table 1), and which constitutes a central landmark of EEG development [18, 31], and suggests that in addition to the high alpha contribution to the peak frequency shifting of alpha, a more extended shifting to higher frequencies occurs across the different alpha sub-components. However, Cragg et al.  suggested that the shifting in peak frequency is due to a greater contribution of alpha 2. This is possibly true for the age group analyzed by these authors (10–13 years old) but not for our group (peri-adolescent period), given that components scores of the high alpha and mu rhythms were not different in children and young adults (Table 2). Therefore, in our group of age, the increase of frequency in the alpha peak seems to occur for the acceleration of all the four sub-components in the alpha range with age.
A final point which deserves some comments is the previously obtained posterior extension of theta rhythm in children with respect to young adults. It has been proposed that theta band shows a maturational progression from posterior to anterior areas [9, 18]. Theta activity has been described as located in posterior areas in children and could be explained as a precursor of the alpha rhythm in adults [9, 10]. These previous studies used a broad band approach. The present narrow-band analysis showed that a parieto-occipital low alpha appears in both young adults and children. Therefore, it can be proposed from present analysis the existence of an independent parieto-occipital low alpha rhythm, peaking at lower frequencies in children than in young adults, which would be the cause of the apparent parieto-occipital extension of the theta rhythm in children [9, 10].
The present results clearly show that the EEG structure is already present in late childhood, although with a higher PS and lower frequency than young adults. The maturation occurs in an asynchronic manner for different frequencies and locations.
Some important limitations of present report are (i) that present study is only able to capture differences between the different age groups and not inside a certain age group, in fact, a continuous recording for all possible age groups would provide a better description of EEG maturation, (ii) the cohort character of present study, which only measures differences between age groups, which is an indirect measure of maturation. However the normal population character of the subjects in this experiment suggests that the obtained results would have a strong relationship with normal EEG developmental maturation in the peri-adolescent period, and (iii), given the circadian and homeostatic pattern of changes in beta, alpha and theta rhythms , and the lack of control of the recording time, part of the obtained differences could be due to differences in recording time or sleep history between age groups.