In this study, cognitive and behavioral emotion regulation strategies (ERS) are classified by using machine learning models driven by a new local EEG complexity approach so called Frequency Specific Complexity (FSC) in resting-states (eyes-opened (EO), eyes-closed (EC)). According to international 10–20 electrode placement system, FSC is defined as entropy estimations in Alpha (8-12Hz) and Beta (12.5-30Hz) frequency band intervals of non-overlapped short EEG segments to observe local EEG complexity variations at 62 points on scalp surface. The healthy adults who use both rumination and cognitive distraction frequently are included in the 1st groups, while the others who use these strategies rarely are included in the 2nd group with respect to Cognitive Emotion Regulation Questionnaire (CERQ) scores of them. EEG data and CERQ scores are downloaded from publicly available data-base LEMON. In order to test the reliability of the proposed method, five different supervised machine learning methods in addition to two Extreme Learning Machine models are examined with 5-fold cross-validation for discrimination of the contrasting groups. The highest classification accuracy (CA) of 99.47% is provided by Class-specific Cost Regulation Extreme Learning Machines in EC state. Regarding cortical regions (anterio-frontal, central, temporal, parieto-occipital), the regional FSC estimations did not provide the higher performance, however, corresponding statistical distribution shows the decrease in EEG complexity at mostly anterior cortex in the 1st group characterized by maladaptive rumination. In conclusion, FSC can be proposed to investigate cognitive dysfunctions often caused by the use of rumination.
· In the present study, a new quantitative single-channel EEG marker called as Frequency Specific Complexity for classification of maladaptive rumination at resting-state.
· The new findings show that maladaptive rumination cause decreases neuronal complexity at mostly anterior regions.
DISCUSSION AND CONCLUSION:
In the present study, a new indicator so called FSC has been proposed for classification of maladaptive rumination in resting-state, since electrophysiological findings commonly reveal close relations between mental disorders and neuro-functional network across the whole cortex even in the resting state , , . Usefulness of WE and AE in computing frequency band specific EEG complexity is tested for estimation of accurate FSC. Then, the reliability of FSC is tested by examining supervised machine learning models and extreme learning machines for detection of maladaptive rumination from resting-state EEG recordings. In addition, statistical differences between the groups are computed through one-way Anova tests according to the corresponding best results.
AE provided considerable better estimations in comparison to WE due to nonlinear nature of EEG segments. AE is a nonlinear statistical quantity of signal irregularity such that its performance depends on the largeness of time-series. Comparison of AE with WE has been studied in EEG analysis with several goals such as detection of driving fatigue ,  and seizure .
Alpha and Beta sub-band specific complexity estimations are averaged to provide FSC in resting-state, since EEG analysis has frequently focused on Alpha and Beta sub-bands for estimation of cognitive skills in resting-state in both past and recent studies , , , . Multi-channel FSC estimations are considered as separate instants (i.e. features) in two-class classification steps. Dimension of the features is identical to the number recording channels that are included in regional features such SET-2, SET-3, SET-4 and SET-5. So, the largest feature set is SET-1 including FSC values estimated from 62-channels over a 1min duration trial divided into short segments of 2s according to 20 individuals in each group. As the number of recording channels increases, both dimension and number of the features also increases in feature sets. Therefore, the best classification performance is obtained through 5-fold cross validated CCR-ELM driven by the largest feature set including FSC values estimated with AE at EC state. In addition to higher CA, CCR-ELM is also faster than the other machine learning models. It can be stated that the classification performance is decreased as the number of features is decreased, however, FSC values estimated with AE are able to detect neuro-functional complexity differences originated from maladaptive rumination at both states (EC, EO) in each feature set classified by any classifier except GNB. In reference study, GNB was reported as less successful than both SVM and k-NN .
In conclusion, the results reveal that maladaptive rumination is highly relevant to FSC estimations (averaged EEG complexity with AE in Alpha and Beta sub-bands of short EEG segments) at mostly EC state at mostly frontal, centro-parietal and parietal locations. In detail, FSC values are decreased in maladaptive ruminations characterized by repetitive thoughts associated with negative feelings. In literature, the left frontal cortex is assumed to be responsible for management of arousal and regulation of stress response, while the right frontal cortex was reported to manage the fight or flight response . However, the current results prove that the whole cortex is more or less affected by cognitive abilities in management of negative emotions. Since, the people with a high tendency to rumination have low ability to manage negative emotions, it is the important issue to correlate maladaptive rumination with resting-state EEG based quantitative indicators.
Since, frequently use of negative ERS called rumination induces deficits in cognitive functioning, the most important hallmark symptoms of major depression disorder, wherein individuals retrieve and repetitively rehearse autobiographical and negatively valance content about both past and current issues , . Moreover, persons who use frequently rumination in daily life, have trouble with sustained attention. Thus, maladaptive rumination is one of the most problematic cognitive symptoms in adults. The present study propose a new quantitative tool to understand the neural mechanism underlying repetitive negative thoughts triggered by depressive feelings.