Cross-Entropy-Based Assessment of Mental Workloads Using Two Prefrontal EEG Channels

This study examined the mechanisms that enhance the overall accuracy of mental workload assessment by synergizing entropy within and between EEG channels. First, we filtered the EEG signals recorded from prefrontal channels and divided them into sub-bands. Next, we derived cross-approximate entropy (XApEn) and conventional approximate entropy (ApEn) metrics from each sub-band. Finally, these derived features were input into an AdaBoost classifier for mental workload assessment. Comparing the classification results reveals that the combination of XApEn and ApEn outperformed the separate employment of XApEn or ApEn, achieving a higher mean accuracy of 82% vs. 72% and 70%, respectively. The results show that interaction between the prefrontal EEG channels is important when assessing mental workload.