The determination of human creative thinking by employing machine learning classification on EEG signals.

Background and objective: Traditional creativity assessments are limited by subjectivity and high labor costs. Although machine learning (ML) offers objective alternatives, its application to EEG-based creativity evaluation remains scarce. This study aimed to classify high and low creative thinking from EEG signals using ML. Methods: One hundred forty participants completed the Alternative Uses Task during EEG recording. Three independent raters (none were authors) evaluated response originality using the Consensus Assessment Technique on a 1-to-5 scale; mean scores were dichotomized at the median into high- and low-creativity labels (996 and 1,096 trials, respectively, from 2,092 valid trials). Classification features included alpha-band Power Spectral Density (PSD), Approximate Entropy, Sample Entropy, and a combined feature set. Six classifiers-Support Vector Machine (SVM), Quadratic Discriminant Analysis (QDA), Logistic Regression (LogR), Decision Tree (DT), XGBoost, and LightGBM-were trained and evaluated using a 10-fold cross-validation strategy. To prevent subject-level information leakage, a Leave-One-Subject-Out (LOSO) validation was additionally conducted. Results: All six classifiers effectively distinguished creativity levels. Under 10-fold cross-validation, SVM achieved optimal performance using Approximate Entropy or Sample Entropy ( Conclusion: ML effectively classifies creativity from EEG signals. The superior performance of entropy features, supported by both trial-level and subject-independent validation, highlights the robustness of the proposed approach and its potential for developing objective, scalable creativity assessment tools.