The aim of the study was to find an objective indicator for evaluation of occupational stress. For this purpose, the electroencephalographic (EEG) spectral asymmetry index (SASI) was applied to estimate the differences between leaders and non-leaders. The experiments were performed on a group of 82 healthy volunteers who were divided into two subgroups of leaders and non-leaders taking into account whether their position comprised the leadership role or not. The resting eyes closed EEG signal was recorded and the signal in channel Pz was selected for calculation of SASI. The results indicated higher SASI values for the subgroup of leaders when compared to non-leaders and the difference between the subgroups was statistically significant. Higher SASI values could indicate increased psychological stress in leaders group and SASI could be a promising method in occupational health analysis.

The aim of the current study is to compare the degree of electroencephalogram (EEG) nonlinear coupling in different frequency bands and segment lengths. EEG recordings from 37 healthy volunteers during eyes-closed resting state are analyzed in six different frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), gamma (30-45 Hz) and total (1-45 Hz) frequency band. Multivariate surrogate data method is used to estimate the degree of EEG nonlinear coupling. The null hypothesis that time series were generated by a linear process was rejected by statistically comparing the nonlinear statistics calculated for original and surrogate data sets. Synchronization likelihood is used as a nonlinear estimate of functional connectivity and three different segment lengths are inspected: 5.12, 10.24 and 20.48 seconds. As a result of the study, the degree of nonlinear coupling increased as the length of the segment increased and was limited to 6% for shorter and 20% for longer segments. Nonlinear coupling was most dominant in total, alpha, beta and theta frequency bands.

This study aims to clarify whether classification accuracy between major depressive disorder (MDD) and healthy subjects increases with complementary use of graph theoretical measures to functional connectivity. Electroencephalography (EEG) signals were recorded from 37 unmedicated MDD subjects (21 female, 16 male) and 37 gender and age matched control subjects. Signals were recorded during eyes-closed resting state from 30 EEG channels. Six frequency bands were analysed: delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), gamma (30-45 Hz) and total (1-45 Hz) frequency band. Coherence for estimation of functional connectivity and three graph theory measures, clustering coefficient (C), characteristic path length (L) and small-worldness (S), were calculated. Feature selection was conducted with two algorithms: genetic algorithm (GA) and sequential feature selection (SFS). Support Vector Machine (SVM) with 10-fold cross-validation was used for classification. Repeated cross-validation test and McNemar's test were used to compare classification accuracies. The results of the study indicate that adding graph theoretical measures to functional connectivity does not significantly increase classification accuracy for distinguishing MDD and healthy subjects. These results may suggest causal connection between abnormalities in functional network organization and functional connectivity.

The aim of the study was to find gender differences in brain functional connectivity in major depressive disorder (MDD). The experiments were performed on 37 unmedicated subjects with MDD (16 male, 21 female) and 37 age and gender-matched healthy subjects. Electroencephalogram (EEG) was recorded from 30 channels and filtered into six frequency bands: whole frequency band (1-45 Hz), delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz) and gamma (30-45 Hz). Synchronization likelihood (SL) was used to estimate functional connectivity. The experimental results indicated that 1) SL of male MDD subjects was significantly increased in the theta and beta frequency bands compared to control group and 2)SL of male MDD subjects was significantly increased in the theta frequency band compared to female MDD subjects. No significant difference was found between female MDD subjects and female control group as well as between healthy male and female subjects. Although similar trend occurred in case of both genders, the results of the study indicate a significant difference between EEG of male and female MDD.

There is a high demand for objective indicators in diagnosis of depression as diagnosis of depression is still based on psychiatrist's subjective judgment. A nonlinear method Lempel Ziv Complexity (LZC) has been previously successfully used for detection of neuronal or mental disorders based on electroencephalographic (EEG) signals. However, the method overlooks the high frequency content of EEG signals. Therefore, this study is aimed to find out whether the use of Multiscale Lempel Ziv Complexity (MLZC), considering also high frequencies, could overcome the limitations of LZC and better differentiate depression. In current study the EEG recordings were carried out on the groups of depressive and healthy subjects of 11 volunteers each. The LZC and MLZC were calculated on resting EEG signals in eyes open condition from 30 channels at a length of 2 minutes. The results revealed the incapability of traditional LZC to differentiate depressive subjects from healthy controls in eyes open condition, while MLZC differentiated two groups in numerous channels at different frequencies, giving the highest classification accuracy in channel F3 (86 %) at frequencies 9 and 15.5 Hz. The results indicate that the high frequency information, which is lost in calculation of traditional LZC, has a great value in differentiating between depressive and control groups.

The aim of the study was to evaluate the applicability of electroencephalogram (EEG) spectral asymmetry index (SASI) for discrimination of the effect of negative and positive emotions on human brain bioelectrical activity. SASI has been previously proposed as a method to detect depression based on the balance of EEG theta and beta frequency band powers. Emotions were evoked on 22 healthy subjects using emotional pictures portraying humans from International Affective Picture System (IAPS) and late response to stimuli was examined (1700-2200 ms). Electroencephalogram (EEG) was recorded in 30 channels divided into 10 brain regions: left frontal, right frontal, left temporal, right temporal, frontal, frontocentral, central, centroparietal, parietal and occipital. Negative stimuli, compared to neutral stimuli, significantly increased SASI in frontocentral, central, centroparietal, parietal and occipital areas. Positive stimuli, compared to neutral stimuli, significantly decreased SASI in left temporal, centroparietal, parietal and occipital areas. The results indicate that SASI provides a good discrimination between the effects of negative, neutral and positive emotions on human EEG.