We introduce an experimental and machine learning framework for investigating whether various human physiological parameters such as heart rate, respiration rate, blood pressure, and skin conductance, as well as brain activity inferred from functional near-infrared spectroscopy or electroencephalogram sufficient to isolate systemic cognitive states such as workload, distraction, or mind wandering among others. We apply various state-of-the-art machine learning techniques to learn classifiers associated with systemic cognitive states like cognitive load, distraction, sense of urgency, mind wandering, and interference. While the classification success of these standard methods was modest, the results nevertheless can serve as a baseline for evaluating future efforts to improve classification, especially methods that take contextual aspects such as task and environmental states into account.
@article{scheutzetal23tics,
title={Estimating Systemic Cognitive States from a Mixture of Physiological and Brain Signals},
author={Matthias Scheutz and Shuchin Aeron and Ayca Aygun and J.P. de Ruiter and Sergio Fantini and Cristianne Fernandez and Zachary Haga and Thuan Nguyen and Boyang Lyu},
year={2023},
journal={Topics in Cognitive Science},
url={https://hrilab.tufts.edu/publications/scheutzetal23tics.pdf}
}