Prediction of dispositional dialectical thinking from resting‐state electroencephalography

Abstract

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.

The Neural Basis of Self-ambivalence: An ERP Study

“…Similarly, this result is similar to the study using fMRI combined with a self-reference paradigm to study the dialectical self-processing of dACC brain region (Wang et al, 2016). The relative activation of dACC brain area was signi cantly correlated with the score of the DSS scale, which further con rmed the strong relationship between the dorsolateral anterior cingulate cortex and DSS (Huang et al, 2021;Wang et al, 2016).…”

Hypotheses Testing of Functional Principal Components

“…Data analytical tools for computing FPCs and FPC scores are collectively referred to as Functional Principal Components Analysis (FPCA), a simplifying preliminary step for many interesting applications involving trajectories {η i (•)} n i=1 as independent variables, see Hall and Hosseini-Nasab (2006), Aue et al (2015) and Shang (2017). Typically FPCA first estimates FPCs and eigenvalues as eigenfunctions and eigenvalues of some estimated G (•, •), and subsequently FPC scores, see Ramsay and Sliverman (2005), Horváth and Kokoszka (2012), Shang (2014), Zhang et al (2020), and Huang et al (2021). Rigorous inference for functional regression models remains difficult if FPC scores estimated from eigen equations are used as predictor variables in place of the true ones, because the differences between the true and estimated FPC scores are of order n −1/2 only implicitly.…”