Nonsuicidal Self-Injury and Its Mediation Effect on the Association Between Posttraumatic Stress Disorder, Depression, and Suicidal Behavior in Firefighters

Objective@#This study aimed to investigate the prevalence, clinical characteristics, and the correlates of nonsuicidal self-injury (NSSI) in firefighters. We also investigated the mediating role of NSSI frequency in the association between posttraumatic stress disorder (PTSD), depression, and suicidal behavior. @*Methods@#A total of 51,505 Korean firefighters completed a web-based self-reported survey, including demographic and occupational characteristics, NSSI, PTSD, depression, and suicidal behavior. Multivariable logistic regression analyses and serial mediation analyses were performed. @*Results@#The 1-year prevalence of NSSI was 4.67% in Korean firefighters. Female gender, the presence of recent traumatic experience, and PTSD and depression symptoms were correlated with NSSI. Serial mediation analyses revealed that NSSI frequency mediated the association between PTSD, depression, and suicidal behavior; it indicates more severe PTSD was sequentially associated with more severe depression symptoms and more frequent NSSI, leading to higher risk of suicidal behavior. @*Conclusion@#NSSI is prevalent and may play a significant mediating role when PTSD is associated with suicidal behavior in firefighters. Our results imply the need for screening and early intervention of NSSI in firefighters.

Motor imagery EEG classification with optimal subset of wavelet based common spatial pattern and kernel extreme learning machine.

Performance of motor imagery based brain-computer interfaces (MI BCIs) greatly depends on how to extract the features. Various versions of filter-bank based common spatial pattern have been proposed and used in MI BCIs. Filter-bank based common spatial pattern has more number of features compared with original common spatial pattern. As the number of features increases, the MI BCIs using filter-bank based common spatial pattern can face overfitting problems. In this study, we used eigenvector centrality feature selection method, wavelet packet decomposition common spatial pattern, and kernel extreme learning machine to improve the performance of MI BCIs and avoid overfitting problems. Furthermore, the computational speed was improved by using kernel extreme learning machine.