Machine learning classification of maladaptive rumination and cognitive distraction in terms of frequency specific complexity


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Aydın S. , Akın B.

Biomedical Signal Processing and Control, vol.77, 2022 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 77
  • Publication Date: 2022
  • Doi Number: 10.1016/j.bspc.2022.103740
  • Title of Journal : Biomedical Signal Processing and Control
  • Keywords: EEG complexity, Brain biophysics, Emotion regulation, Entropy, Machine learning, DYNAMIC FUNCTIONAL CONNECTIVITY, EMOTION-REGULATION, APPROXIMATE ENTROPY, ALZHEIMERS-DISEASE, EPILEPTIC SEIZURE, EEG SIGNALS, DEPRESSION, DISORDERS, SUPPRESSION, ATTENTION

Abstract

https://www.sciencedirect.com/science/article/pii/S1746809422002622?via%3Dihub 

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.

Highlights

·        In the present study, a new quantitative single-channel EEG marker called as Frequency Specific Complexity for classiffication of maladaptive rumination at resting-state.

·        The reliability of the proposed method has been provided by using seven different 5-fold cross-validated classiffiers with respect to both states (eyes-opened vs eyes-closed) and cortical regions.

·        The new findings show that maladaptive rumination cause decrease neuronal complexity at mostly anterior regions.