A New Feature For Cross-Day Psychophysiological Workload Estimation

Classification of operator functional state for workload estimation using electroencephalograph (EEG) has proven difficult in cross-day scenarios due to non-stationarity of the feature and target distributions. This study analyzes multi-day data collected from a Multi-Attribute Task Battery (MATB) workload study using a new feature generation methodology which examines not just the average power, but also the variability of the power distribution in the clinical frequency bands over a 10 second sliding temporal window. High versus low workload levels were predicted for day five of the study based on training three traditional classifiers-Linear Discriminant Analysis (LDA), random forest, and K-Nearest Neighbors (KNN)-on the first four days' results. Frequency-domain power distribution variance was statistically significant between conditions, suggesting it as a salient feature. Including variance as a feature enabled a cross-day workload classification accuracy improvement of 5.8% above models only using mean power. Furthermore, the individual classifiers were combined into a time-smoothed composite classifier which capitalized on the differences in features selected in the models to improve overall classification accuracy to greater than 80%.