Prediction of dispositional dialectical thinking from resting-state electroencephalography

This study aims to explore the possibility of predicting the dispositional level of dialectical thinking using resting-state electroencephalography signals. Thirty-four participants completed a self-reported measure of dialectical thinking, and their resting-state electroencephalography was recorded. After wave filtration and eye movement removal, time-frequency electroencephalography signals were converted into four frequency domains: delta (1-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), and beta (13-30 Hz). Functional principal component analysis with B-spline approximation was then applied for feature reduction. Five machine learning methods (support vector regression, least absolute shrinkage and selection operator, K-nearest neighbors, random forest, and gradient boosting decision tree) were applied to the reduced features for prediction. The model ensemble technique was used to create the best performing final model. The results showed that the alpha wave of the electroencephalography signal in the early period (12-15 s) contributed most to the prediction of dialectical thinking. With data-driven electrode selection (FC1, FCz, Fz, FC3, Cz, AFz), the prediction model achieved an average coefficient of determination of 0.45 on 200 random test sets. Furthermore, a significant positive correlation was found between the alpha value of standardized low-resolution electromagnetic tomography activity in the right dorsal anterior cingulate cortex and dialectical self-scale score. The prefrontal and midline alpha oscillations of resting electroencephalography are good predictors of the dispositional level of dialectical thinking, possibly reflecting these brain structures' involvement in dialectical thinking.