Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people.

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.

Correlation between electroencephalographic markers in the healthy brain.

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.

Changes in EEG Measures of a Recipient of the mRNA COVID-19 Vaccine - A Case Study.

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.

Ensemble Approach for Detection of Depression Using EEG Features.

Depression is a public health issue that severely affects one's well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel-Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.

Long-term stability of resting state EEG-based linear and nonlinear measures.

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.

Reliability of Electroencephalogram-Based Individual Markers - Case Study.

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.

Negative Correlation Between Functional Connectivity and Small-Worldness in the Alpha Frequency Band of a Healthy Brain.

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.

Assessment of Objective Symptoms of Depression in Occupational Health Examination.

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 Pz. 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.

Surrogate Data Method Requires End-Matched Segmentation of Electroencephalographic Signals to Estimate Non-linearity.

The aim of the study is to clarify the impact of the strong cyclic signal component on the results of surrogate data method in the case of resting electroencephalographic (EEG) signals. In addition, the impact of segment length is analyzed. Different non-linear measures (fractality, complexity, etc.) of neural signals have been demonstrated to be useful to infer the non-linearity of brain functioning from EEG. The surrogate data method is often applied to test whether or not the non-linear structure can be captured from the data. In addition, a growing number of studies are using surrogate data method to determine the statistical threshold of connectivity values in network analysis. Current study focuses on the conventional segmentation of EEG signals, which could lead to false results of surrogate data method. More specifically, the necessity to use end-matched segments that contain an integer number of dominant frequency periods is studied. EEG recordings from 80 healthy volunteers during eyes-closed resting state were analyzed using multivariate surrogate data method. The artificial surrogate data were generated by shuffling the phase spectra of original signals. The null hypothesis that time series were generated by a linear process was rejected by statistically comparing the non-linear statistics calculated for original and surrogate data sets. Five discriminating statistics were used as non-linear estimators: Higuchi fractal dimension (HFD), Katz fractal dimension (KFD), Lempel-Ziv complexity (LZC), sample entropy (SampEn) and synchronization likelihood (SL). The results indicate that the number of segments evaluated as non-linear differs in the case of various non-linear measures and changes with the segment length. The main conclusion is that the dependence on the deviation of the segment length from full periods of dominant EEG frequency has non-monotonic character and causes misleading results in the evaluation of non-linearity. Therefore, in the case of the signals with non-monotonic spectrum and strong dominant frequency, the correct use of surrogate data method requires the signal length comprising of full periods of the spectrum dominant frequency. The study is important to understand the influence of incorrect selection of EEG signal segment length for surrogate data method to estimate non-linearity.

After-effect induced by microwave radiation in human electroencephalographic signal: a feasibility study.

PURPOSE: This feasibility study is aimed to clarify the possibility of detection of microwave radiation (MWR)-induced event related potential (ERP) in electroencephalographic (EEG) signal. METHODS: To trigger onset and offset effects in EEG, repetitive MWR stimuli were used. Four 30-channel EEG recordings on a single subject were performed, each about one month apart. The subject was exposed to 450 MHz MWR modulated at 40 Hz at the 1 g peak spatial average specific absorption rate of 0.3 W/kg. During a recording, 40 cycles of 30 s on-off MWR exposure were used. The artifact-free responses to 126 MWR-ON stimuli and 134 MWR-OFF stimuli were averaged over stimuli and channels. RESULTS: Regarding EEG signals locked to MWR-OFF stimulus, the enhanced signal level at alpha frequency band and about twice higher signal to noise ratio at 200 to 440 ms after the stimulus have been detected. No remarkable response in EEG signals locked to MWR-ON stimulus. CONCLUSIONS: The detection of offset effect confirms that there should be an imprint generated by MWR in brain. The results of this preliminary study provide evidence for the detection of MWR-induced ERP in EEG signal and encourage further research in this direction.

Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis.

BACKGROUND AND OBJECTIVE: Depressive disorder is one of the leading causes of burden of disease today and it is presumed to take the first place in the world in 2030. Early detection of depression requires a patient-friendly inexpensive method based on easily measurable objective indicators. This study aims to compare various single-channel electroencephalographic (EEG) measures in application for detection of depression. METHODS: The EEG recordings were performed on a group of 13 medication-free depressive outpatients and 13 gender and age matched controls. The recorded 30-channel EEG signal was analysed using linear methods spectral asymmetry index, alpha power variability and relative gamma power and nonlinear methods Higuchi's fractal dimension, detrended fluctuation analysis and Lempel-Ziv complexity. Classification accuracy between depressive and control subjects was calculated using logistic regression analysis with leave-one-out cross-validation. Calculations were performed separately for each EEG channel. RESULTS: All calculated measures indicated increase with depression. Maximal testing accuracy using a single measure was 81% for linear and 77% for nonlinear measures. Combination of two linear measures provides the accuracy of 88% and two nonlinear measures of 85%. Maximal classification accuracy of 92% was indicated using mixed combination of three linear and three nonlinear measures. CONCLUSIONS: The results of this preliminary study confirm that single-channel EEG analysis, employing the combination of measures, can provide discrimination of depression at the level of multichannel EEG analysis. The performed study shows that there is no single superior measure for detection of depression.