Mental state monitoring is a critical component of current and future human-machine interfaces, including semi-autonomous driving and flying, air traffic control, decision aids, training systems, and will soon be integrated into ubiquitous products like cell phones and laptops. Current mental state assessment approaches supply quantitative measures, but their only frame of reference is generic population-level ranges. What is needed are physiological biometrics that are validated in the context of task performance of individuals. Using curated intake experiments, we are able to generate personalized models of three key biometrics as useful indicators of mental state; namely, mental fatigue, stress, and attention. We demonstrate improvements to existing approaches through the introduction of new features. Furthermore, addressing the current limitations in assessing the efficacy of biometrics for individual subjects, we propose and employ a multi-level validation scheme for the biometric models by means of k-fold cross-validation for discrete classification and regression testing for continuous prediction. The paper not only provides a unified pipeline for extracting a comprehensive mental state evaluation from a parsimonious set of sensors (only EEG and ECG), but also demonstrates the use of validation techniques in the absence of empirical data. Furthermore, as an example of the application of these models to novel situations, we evaluate the significance of correlations of personalized biometrics to the dynamic fluctuations of accuracy and reaction time on an unrelated threat detection task using a permutation test. Our results provide a path toward integrating biometrics into augmented human-machine interfaces in a judicious way that can help to maximize task performance.
Keywords: EEG; N-back recall; attention; computational models; machine learning; mental fatigue; stress; validation.