Evaluating Mental Workload Through Cross-Entropy Analysis of Two Prefrontal EEG Channels

The objective of this study was to explore whether cross-entropy metrics provide further insights in comparison to conventional entropy, aiming to enhance the accuracy of mental workload assessment. We initially filtered and segmented EEG signals from two prefrontal channels and decomposed the signals into subbands. Afterward, we calculated a range of cross-entropy and traditional entropy metrics for each sub-band. Finally, these extracted features were fed into an AdaBoost classifier to evaluate mental workload levels. The comparison of classification results demonstrated that integrating cross-sample entropy with sample entropy notably enhanced accuracy by 10%, reaching 84%. Further, utilizing the complete set of cross-entropy metrics yielded an accuracy of 84.5%.