A data-driven framework for learners’ cognitive load detection using ECG-PPG physiological feature fusion and XGBoost classification

Cognitive load condition is of great significance for judging learners’ learning state and improving the learning and teaching effects. This paper proposed a feature fusion based processing framework for high cognitive load detection, which includes heart rate variability (HRV) and pulse rate variability (PRV) acquisition, data preprocessing, feature extraction, feature selection, feature fusion by linear feature dependency modeling (LFDM) and high cognitive load detection by XGBoost classifier. This paper experiment on simulated learning paradigm, and the experimental results show that the proposed framework for detection of high cognitive load outperforms conventional processing approaches that uses HRV or PRV only. This paper compared the effects of using different feature fusion algorithms (PCT, SKRRR, ADMM, LFDM) and different classification algorithms (KNN, SVM, DT, RF, XGBoost), and the final proposed framework outperforms other schemes. The proposed framework achieves approximately 97.2% accuracy of high cognitive load detection.