Impact of Temporal Window Shift on EEG-Based Machine Learning Models for Cognitive Fatigue Detection

In our study, we examine how the temporal window shift—the step between consecutive analysis windows—affects EEG-based cognitive fatigue detection while keeping the window length fixed. Using a reference workload dataset and a pipeline that includes preprocessing and feature extraction, we vary the shift to control segment overlap and, consequently, the number and independence of training samples. We evaluate six machine-learning models (decision tree, random forest, SVM, kNN, MLP, and a transformer). Across the models, smaller shifts generally increase accuracy and F1 score, consistent with the larger sample count; however, they also reduce sample independence and can inflate performance if evaluation splits are not sufficiently stringent. Class-wise analyses reveal persistent confusion for the moderate-fatigue class, the severity of which depends on the chosen shift. We discuss the methodological trade-offs, provide practical recommendations for choosing and reporting shift parameters, and argue that temporal segmentation decisions should be treated as first-class design choices in EEG classification. Our findings highlight the need for transparent reporting of window length, shift/overlap, and subject-wise evaluation protocols to ensure reliable and reproducible results in cognitive fatigue detection. Our conclusions pertain to subject-wise generalization on the STEW dataset; cross-dataset validation is an important next step.