predicting posttraumatic growth in covid-19 patients using electroencephalogram signals

Purpose: the present study aimed to investigate the quantitative pattern of brain waves with post-traumatic growth dimensions in patients admitted due to coronavirus disease (covid-19). post-traumatic growth is the mental experience of positive psychological changes caused by the individual as a result of coping with challenging situations. materials and methods: in this study, 66 individuals with covid-19 who were admitted to baharloo hospital in tehran as a stressful event were selected by convenience sampling and completed a post-traumatic growth inventory (ptgi) and their brain waves in rest were recorded. results: the results showed that brain components are a good predictor of post-traumatic growth dimensions. alpha-parietal, f3-sensorimotor rhythm (f3-smr) and alpha asymmetry predicted new possibilities component, alpha-f3 and alpha asymmetry predicted relating to others component, f4-smr predicted spiritual change component and alpha asymmetry significantly predicted the total post-traumatic growth score. also, quantitative electroencephalogram (qeeg) components did not significantly predict the appreciation of life and personal strength component. conclusion: according to the results, it can be said that more objective instruments such as electroencephalogram )eeg( have good predictive power in complex psychological and multidimensional cases such as post-traumatic growth. the results of this study confirm the hypothesis that post-traumatic growth may reflect a process of active struggle to achieve new goals and perspectives. accordingly, especially the more guided dimensions of post-traumatic growth (e.g., the new possibilities dimension) may be associated with the asymmetry of the frontal lobe of the brain. in contrast, the dimensions of appreciation of life and personal strength were not predicted by the brain component; these two components were slightly more abstract than the others and may lead to more / less neural network activity in functional magnetic resonance imaging (fmri) that is more observable.