Objective: We have previously demonstrated a negative correlation between electroencephalographic (EEG) alpha functional connectivity (FC) and small-world organization in healthy individuals, suggesting a compensatory mechanism that maintains efficient information processing. It remains unclear whether this relationship persists in major depressive disorder (MDD), a condition associated with altered brain network dynamics. We aim to investigate whether the FC–SW correlation observed in healthy subjects is present in MDD patients. Methods: Eyes-closed EEG (30 channels, 6 min) was recorded from 37 unmedicated MDD patients. Alpha (8–12 Hz) FC was assessed with magnitude-squared coherence (MSC) and the imaginary part of coherency (ICOH). Small-worldness (SW) was computed across network densities ranging from 10

The clinical applicability of electroencephalography (EEG) relies on the reliability and temporal stability of its measures. While the reliability of linear EEG measures is well established, the long-term stability of both linear and nonlinear measures at the individual level, as well as interindividual variability, remains underexplored. This study evaluated the one-year stability of EEG absolute band powers (theta, alpha, beta, and gamma) and nonlinear measures (Higuchi's fractal dimension, Lempel-Ziv complexity, detrended fluctuation analysis, and in-phase Matrix Profile) across 12 monthly EEG recordings in nine healthy males aged 26-49. Intraclass correlation coefficients (ICCs) indicated excellent reliability across all measures, although beta power showed slightly reduced ICCs in temporal regions and gamma power demonstrated lower reliability in peripheral sites. At the individual level, nonlinear measures showed greater temporal stability than EEG band powers. Although a few individuals, particularly in band power measures, exhibited annual fluctuations comparable to or exceeding interindividual variability, most participants demonstrated consistent EEG profiles over time. These findings support the use of nonlinear EEG measures in longitudinal research and indicate their potential for developing personalized EEG-based neural biomarkers. They also highlight the importance of estimating expected individual variability when designing individualized monitoring approaches, as high reliability at the group level does not preclude substantial within-subject variability in some cases.

Functional connectivity and small-worldness are widely used techniques to study brain network organization, and previous research has shown a negative correlation between them in the alpha frequency band. This study examines how specific electroencephalography (EEG) alpha-band connections contribute to the balance between functional connectivity and small-worldness. EEG was recorded from 80 healthy subjects in a resting-state, eyes-closed condition. Functional connectivity was estimated using magnitude-squared coherence (MSC), imaginary part of coherency (ICOH), and synchronization likelihood (SL), and small-worldness was calculated for each of these measures. The strongest negative correlation between functional connectivity and small-worldness was found in frontal-occipital and frontal connections. Given that these regions have been linked to the default mode network (DMN), the observed correlation may reflect DMN activity. These findings highlight a balance between functional network structure and connectivity and provide insight into how resting-state networks maintain functional organization. Future research should investigate how this balance shifts during cognitive tasks and in neurological disorders.Clinical Relevance— The current study is a step in understanding the physiology of a healthy brain networks, potentially aiding in the early detection and prevention of brain disorders

Glucose metabolism is an important factor in human physiology and the main source of energy for the human brain. Low blood glucose concentration (hypoglycemia) will cause several neurological and cardiological symptoms. High concentration (hyperglycemia) is an indicator of a higher risk to diabetes. The current study aims to investigate the correlation between electroencephalographic signal (EEG) functional connectivity and fasting blood glucose concentration in healthy people with normal blood glucose concentration. The present study was carried out on a group of forty-four healthy volunteers. The resting-state eyes-closed 30-channel EEG was recorded for 6 minutes and blood samples were collected from the subjects on the same morning. To describe the functional connectivity, magnitude-squared coherence (MSC) was calculated between all channels in delta, theta, alpha, beta and gamma EEG frequency bands. The negative correlation between MSC and fasting blood glucose concentration was statistically significant in delta and gamma bands. The results of the study suggest that the variations in normal blood glucose concentration can affect brain functioning.Clinical Relevance— This is a step in the development of novel biomarkers for monitoring neurological and cognitive function, potentially aiding in the early detection and prevention of metabolic and neurocognitive disorders.

This study aims to investigate the association between the natural level of blood biomarkers and electroencephalographic (EEG) markers. Resting EEG theta, alpha (ABP), beta, and gamma frequency band powers were selected as linear EEG markers indicating the level of EEG power, and Higuchi’s fractal dimension (HFD) as a nonlinear EEG complexity marker reflecting brain temporal dynamics. The impact of seven different blood biomarkers, i.e., glucose, protein, lipoprotein, HDL, LDL, C-reactive protein, and cystatin C, was investigated. The study was performed on a group of 52 healthy participants. The results of the current study show that one linear EEG marker, ABP, is correlated with protein. The nonlinear EEG marker (HFD) is correlated with protein, lipoprotein, C-reactive protein, and cystatin C. A positive correlation with linear EEG power markers and a negative correlation with the nonlinear complexity marker dominate in all brain areas. The results demonstrate that EEG complexity is more sensitive to the natural level of blood biomarkers than the level of EEG power. The reported novel findings demonstrate that the EEG markers of healthy people are influenced by the natural levels of their blood biomarkers related to their everyday dietary habits. This knowledge is useful in the interpretation of EEG signals and contributes to obtaining information about people quality of life and well-being.

The correlation between electroencephalographic (EEG) and electrocardiographic (ECG) signals can provide important information about cardiovascular regulation. The current study aims to investigate the dependence of the correlation between EEG and heart rate variability (HRV) on cardiovascular indicators. The signals of a group of 30 subjects were divided into two groups of 15 subjects according to the lower and higher indicators of blood pressure and cholesterol. Relative EEG frequency band powers were calculated in theta, alpha, and beta frequency bands. From power spectral analysis of HRV, low frequency (LF) power, high frequency (HF) power and LF/HF were calculated. In the current study, the correlation between EEG and HRV is detected by two HRV features, LF and HF, in the theta band, and also by two features, HF and LF/HF, in the alpha band. The correlation by one HRV feature, LF/HF, is revealed in the beta band. The effect of cardiovascular features is reflected by LF and HF features in the theta band and by HF in the alpha band. The novel finding that even a small increase in cardiovascular features (blood pressure and cholesterol) can affect cardio-neuronal regulation is important and needs further investigation.

Background and Objective: Major depressive disorder (MDD) is the leading cause of disability worldwide. Reliable detection of MDD is the basis for early and successful intervention in treating the disorder and preventing disability. We introduce a novel feature extraction method, the in-phase matrix profile (pMP), which is specifically adapted for electroencephalographic (EEG) signals. Methods: The pMP characterizes general self-similarity of an EEG signal. The method extracts overlapping one-second-long subsegments from an EEG signal segment, calculates Euclidean distances between all possible subsegment pairs, and subsequently uses the distance values, where subsegments are most in phase, to calculate pMP. The method was applied to the resting-state eyes-closed EEG data of an MDD group and age- and gender-matched healthy controls (66 subjects). Higuchi's fractal dimension (HFD) values were calculated for the same groups for comparison. Results: Both pMP and HFD values were higher in MDD. The pMP successfully distinguished MDD and control group in all 30 EEG channels. In contrast, HFD resulted in statistically significant group distinguishability in 13 (43%) channels located mainly in the central region of the head. The highest classification accuracy for pMP was 73% and for HFD 67%. Conclusion: The present article shows that pMP outperforms HFD in detecting MDD and is a promising method for future MDD studies. Significance: The pMP is a sensitive parameter-free method for detecting MDD that can be used in future studies and is a potential method to reach clinical use for diagnosing MDD.

Mental disorders have an increasing tendency and represent the main burden of disease to society today. A wide variety of electroencephalographic (EEG) markers have been successfully used to assess different symptoms of mental disorders. Different EEG markers have demonstrated similar classification accuracy, raising a question of their independence. The current study is aimed to investigate the hypotheses that different EEG markers reveal partly the same EEG features reflecting brain functioning and therefore provide overlapping information. The assessment of the correlations between EEG signal frequency band power, dynamics, and functional connectivity markers demonstrates that a statistically significant correlation is evident in 37 of 66 (56%) comparisons performed between 12 markers of different natures. A significant correlation between the majority of the markers supports the similarity of information in the markers. The results of the performed study confirm the hypotheses that different EEG markers reflect partly the same features in brain functioning. Higuchi's fractal dimension has demonstrated a significant correlation with the 82% of other markers and is suggested to reveal a wide spectrum of various brain disorders. This marker is preferable in the early detection of symptoms of mental disorders.

PURPOSE:The current paper is aimed to discuss the principles and criteria for health protection to radiofrequency electromagnetic field (RF EMF) considering both thermal and non-thermal mechanisms to evaluate the reasonable level for the limits relevant to control the level of RF EMF for the general public in the living environment. The study combines the conclusions of analyses published in recent reviews on RF EMF effects and the data from RF EMF measurements in different countries to select the possible criteria and to derive proposals for the health protection limits on the level of RF EMF following the ALARA principle - as low as reasonably achievable. CONCLUSIONS:Consideration of not only energetic but also coherent qualities of RF EMF leads to two different models for determining the impact of non-ionizing radiation on human health. The thermal model, based on absorption of electromagnetic energy, has a threshold limiting the heating of tissues. The non-thermal model, based on the ability of coherent electric fields to introduce biological effects at constant temperature, has no threshold. Therefore, the impact of RF EMF on human health cannot be excluded but can be minimized by limiting the level of the radiation. The limits can be selected based on indirect criteria. The minimal level of RF EMF that has caused a biological effect is about 2 V/m. The level of long-term broadcast radiation is 6 V/m and the people can be assumed to be adapted to that level without observable health problems. The level of RF EMF measured during last years does not exceed 5 V/m and the level is decreasing with newer generations of telecommunication technology. Limiting the level of RF EMF to the peak value of 6 V/m hopefully reduces the health risk to a minimal level people are adapted to and does not restrict the further development of telecommunication technology.

Mental disorders, especially depression, have become a rising problem in modern society. The development of methods and markers for the early detection of mental disorders is an actual problem. Psychological questionnaires are the only tools for evaluating the symptoms of mental disorders in clinical practice today. The electroencephalography (EEG) based non-invasive and cost-effective method seems feasible for the early detection of depression in occupational and family medicine centers and personal monitoring. The reliability of the EEG markers in the early detection of depression assumes their high temporal stability and correlation with the scores of depression questionnaires. The study was been performed on 17 healthy people over three years. Two hypotheses have been evaluated in the current study: first, the temporal stability of EEG markers is close to the stability of the scores of depression questionnaires, and second, EEG markers and depression questionnaires’ scores are not correlated in healthy people. The results of the performed study support both hypotheses: the temporal stability of EEG markers is high and close to the stability of depression questionnaires scores and the correlation between the EEG markers and depression questionnaires scores is not detected in healthy people. The results of the current study contribute to the interpretation of results in depression EEG studies and to the feasibility of EEG markers in the detection of depression.

Purpose The deployment of new 5G NR technology has significantly raised public concerns in possible negative effects on human health by radiofrequency electromagnetic fields (RF EMF). The current review is aimed to clarify the differences between possible health effects caused by the various generations of telecommunication technology, especially discussing and projecting possible health effects by 5G. The review of experimental studies on the human brain over the last fifteen years and the discussion on physical mechanisms and factors determining the dependence of the RF EMF effects on frequency and signal structure have been performed to discover and explain the possible distinctions between health effects by different telecommunication generations.Conclusions The human experimental studies on RF EMF effects on the human brain by 2G, 3G and 4G at frequencies from 450 to 2500 MHz were available for analyses. The search for publications indicated no human experimental studies by 5G nor at the RF EMF frequencies higher than 2500 MHz. The results of the current review demonstrate no consistent relationship between the character of RF EMF effects and parameters of exposure by different generations (2G, 3G, and 4G) of telecommunication technology. At the RF EMF frequencies lower than 10 GHz, the impact of 5G NR FR1 should have no principal differences compared to the previous generations. The radio frequencies used in 5G are even higher and the penetration depths of the fields are smaller; therefore, the effect is rather lower than at previous generations. At the RF EMF frequencies higher than 10 GHz, the mechanism of the effects might differ and the impact of 5G NR FR2 becomes unpredictable. Existing knowledge about the mechanism of RF EMF effects at millimeter waves lacks sufficient experimental data and theoretical models for reliable conclusions. The insufficient knowledge about the possible health effects at millimeter waves and the lack of in vivo experimental studies on 5G NR underline an urgent need for the theoretical and experimental investigations of health effects by 5G NR, especially by 5G NR FR2.

The current study is aimed to evaluate the effect of COVID-19 vaccine on human EEG and the persistence of the effect. Within a one-year-long resting EEG study period, the healthy male subject was administered two Comirnaty doses three weeks apart to prevent COVID-19. Fourteen recordings were acquired from the subject in one year: twelve reference and two post-vaccination recordings after administrating the second dose of Comirnaty. The changes in absolute powers of EEG frequency bands, EEG spectral asymmetry index (SASI), and Higuchi's fractal dimension (HFD) were analyzed. The results indicated a statistically significant increase in absolute gamma power, SASI and HFD values on the fifth day after the vaccination, while the EEG had restored its normal character on the twelfth day after vaccination. These measures seem to have higher sensitivity for the detection of the effects of the vaccine Clinical Relevance- This is the first study evaluating COVID-19 vaccine effect on healthy human EEG. The study indicated that the vaccine disturbs EEG but the impact is not long-lasting.

Purpose This review aims to estimate the threshold of radiofrequency electromagnetic field (RF EMF) effects on human brain based on analyses of published research results. To clarify the threshold of the RF EMF effects, two approaches have been applied: (1) the analyses of restrictions in sensitivity for different steps of the physical model of low-level RF EMF mechanism and (2) the analyses of experimental data to clarify the dependence of the RF EMF effect on exposure level based on the results of published original neurophysiological and behavioral human studies for 15 years 2007-2021. Conclusions The analyses of the physical model of nonthermal mechanisms of RF EMF effect leads to conclusion that no principal threshold of the effect can be determined. According to the review of experimental data, the rate of detected RF EMF effects is 76.7% in resting EEG studies, 41.7% in sleep EEG and 38.5% in behavioral studies. The changes in EEG probably appear earlier than alterations in behavior become evident. The lowest level of RF EMF at which the effect in EEG was detected is 2.45 V/m (SAR = 0.003 W/kg). There is a preliminary indication that the dependence of the effect on the level of exposure follows rather field strength than SAR alterations. However, no sufficient data are available for clarifying linearity-nonlinearity of the dependence of effect on the level of RF EMF. The finding that only part of people are sensitive to RF EMF exposure can be related to immunity to radiation or hypersensitivity. The changes in EEG caused by RF EMF appeared similar in the majority of analyzed studies and similar to these in depression. The possible causal relationship between RF EMF effect and depression among young people is highly important problem.

This preliminary study is aimed to evaluate the stability of various linear and nonlinear EEG measures over three years on healthy adults. The linear measures, relative powers of EEG frequency bands, interhemispheric (IHAS) and spectral (SASI) asymmetries plus nonlinear Higuchi's fractal dimension (HFD) and detrended fluctuation analyses (DFA), have been calculated from the resting state eyes closed EEG of 17 participants during two sessions separated over three years. Our results indicate that the stability is highest for the nonlinear (HFD and DFA) and the linear (relative powers of EEG frequency bands) EEG measures that use the signal from a single EEG channel and frequency band, followed by the SASI employing signals from a single channel and two frequency bands and lowest for the IHAS employing signals from two channels. The result support the prospect of using EEG-based measures in clinical practice.

The aim of the study was to analyze the relationship between resting state electroencephalographic (EEG) alpha functional connectivity (FC) and small-world organization. For that purpose, Pearson correlation was calculated between FC and small-worldness (SW). Three undirected FC measures were used: magnitude-squared coherence (MSC), imaginary part of coherency (ICOH), and synchronization likelihood (SL). As a result, statistically significant negative correlation occurred between FC and SW for all three FC measures. Small-worldness of MSC and SL were mostly above 1, but lower than 1 for ICOH, suggesting that functional EEG networks did not have small-world properties. Based on the results of the current study, we suggest that decreased alpha small-world organization is compensated with increased connectivity of alpha oscillations in a healthy brain.

The aim of this study was to evaluate individual level of natural variability of electroencephalogram (EEG) based markers. Three linear: alpha power variability, spectral asymmetry index, relative gamma power and three nonlinear methods: Higuchi's fractal dimension, detrended fluctuation analysis, and Lempel-Ziv complexity were selected. The markers were evaluated over 15 sessions acquired in 14 months. The results indicate that individual natural variability for five of the selected markers is lower compared to differences between healthy and depressed groups of subjects in our previous studies. The results of the current study suggest that EEG based markers can be applied for evaluation of disturbances in brain activity at individual level.Clinical Relevance-The indicated stability in the current study of widely used EEG-based markers at individual level suggests a promising opportunity to apply EEG as a novel method in diagnoses of brain mental disorders in clinical practice.

The purpose of this preliminary study was to assess the long-term stability of the electroencephalography (EEG) based spectral asymmetry index (SASI) previously proposed as an objective measure for evaluation of depression. Due to high variability between individuals, a long-term study is required to examine how SASI changes over longer period of time for an individual subject. One healthy adult was surveyed over the period of 15 months. The eyes closed resting state EEG was analyzed over 15 sessions, on average once a month. The SASI was calculated from EEG power spectrum density as a relative difference in powers of higher and lower band of the EEG spectrum maximum. The long-term stability of SASI for an individual was compared to inter-individual variability of the measure within the group of healthy subjects in our previous study. The results demonstrated that the individual long-term variability of SASI is less than inter-individual variability within a group, supporting the possibility of application of SASI for evaluation of depression symptoms for an individual. In future, the investigations should be extended on larger number of subjects to study the individual properties of SASI.

Seasonal alterations in human health, mood, and basal cortisol level have been reported in several studies. Despite the interest in these factors, seasonal changes in brain functional connectivity as a possible base of these phenomena have not been studied before. The aim of the current study is to analyse seasonal effects using two resting electroencephalogram (EEG) functional connectivity measures: magnitude-squared coherence (MSC) and imaginary coherence (iCOH). Recordings from 80 healthy Estonians were used: 25 recordings from spring, 8 from summer, 10 from autumn and 37 from winter months. Eyes-closed resting EEG was recorded from 30 channels using Neuroscan Synamps2 acquisition system and five frequency bands were analysed: delta, theta, alpha, beta and gamma. Multivariate permutation test revealed significant influence of seasons on beta MSC. Furthermore, statistical analysis between different seasons showed increased beta MSCin spring and winter months compared to summer and autumn months, increased beta iCOH in spring, autumn and winter months compared to summer months and increased gamma MSC in spring and summer months compared to autumn months. The increase in beta MSC and iCOH in spring and winter months compared to summer months may be the result of increased stress or deficiency in Vitamin D. Current study is the first to bring out seasonal changes in brain functional connectivity, but the shortcoming of the study is the limited number of recordings in summer and autumn months. Therefore, further studies are required for more reliable results.

Objective: The aim of the study was to assess early symptoms of depression in regular occupational health examination using the objective measures based on electroencephalographic (EEG) signal analysis. Methods: The study was performed on 125 volunteer participants. The resting-state EEG signal was recorded for 7 minutes. The spectral asymmetry index (SASI) and Higuchi fractal dimension (HFD) were calculated in EEG channel P-z. Parallel, the participants were subjected to two psychological tests, observer-rated HAM-D and self-rated EST-Q-D. Results: The SASI revealed depressive symptoms for 64.8%, HFD for 55.2%, HAM-D for 44.8%, and EST-Q-D for 28.8% of participants. Combination of two different measures indicated depression symptoms up to 78.4% of participants. Conclusion: The results of this study confirm the feasibility of indication of early symptoms of depression applying EEG-based objective measures.

An objective indicator based on the asymmetry of electroencephalographic (EEG) signal spectrum has been shown promising for screening of population to discover occupational stress. However, the factors other than stress affect the EEG spectrum. The aim of the current study is to investigate the role of education level on EEG signals’ band relative powers. For this purpose, 18-channel resting eyes-closed EEG was recorded from 30 men having Bachelor or higher education (tertiary education) and 16 men declaring to have lower, upper or post-secondary education (secondary education). For those signals, relative theta, alpha, beta and gamma powers were calculated. The results indicated increase in relative gamma power for the subgroup of men having tertiary education compared to the subgroup of men having secondary education. No significant alterations were revealed in other relative band powers. Higher relative gamma power of men having higher level of education could be related to the higher cognitive load in their everyday life, as widespread gamma activation has been previously demonstrated during cognitive tasks. The results of the current study suggest that the level of education is one of the factors to be taken into account in EEG based evaluation of occupational stress or mental disorders.