Prediction of Dispositional Dialectical Thinking from Resting-State Electroencephalography

• Activity Number: 306 - SPEED: SPAAC SESSION II
• Type: Topic-Contributed
• Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Kun Huang* and Dian Chen and Fei Wang and Lijian Yang
• Companies: Tsinghua University and Tsinghua University and Tsinghua University and Tsinghua University
• Keywords: FDA; resting-EEG; machine learning; alpha wave; dialectical thinking

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 signal 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 principle 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 aver