Brain-Based Indices for User System Symbiosis

The future generation user system interfaces need to be user-centric which goes beyond user-friendly and includes understanding and anticipating user intentions. We introduce the concept of operator models, their role in implementing user-system symbiosis, and the usefulness of brain-based indices on for instance effort, vigilance, workload and engagement to continuously update the operator model. Currently, the best understood parameters in the operator model are vigilance and workload. An overview of the currently employed brain-based indices showed that indices for the lower workload levels (often based on power in the alpha and theta band of the EEG) are quite reliable, but good indices for the higher workload spectrum are still missing. We argue that this is due to the complex situation when performance stays optimal despite increasing task demands because the operator invests more effort. We introduce a model based on perceptual control theory that provides insight into what happens in this situations and how this affects physiological and brain-based indices.We argue that a symbiotic system only needs to intervene directly in situations of under and overload, but not in a high workload situation. Here, the system must leave the option to adapt on a short notice exclusively to the operator. The system should lower task demands only in the long run to reduce the risk of fatigue or long recovery times. We end by indicating future operator model parameters that can be reflected by brain-based indices

The mental machine: Classifying mental workload state from unobtrusive heart rate-measures using machine learning

This paper investigates whether mental workload can be classified in an operator setting using unobtrusive psychophysiological measures. Having reliable predictions of workload using unobtrusive sensors can be useful for adaptive instructional systems, as knowledge of a trainee’s workload can then be used to provide appropriate training level (not too hard, not too easy). Previous work has investigated automatic mental workload prediction using biophysical measures and machine learning, however less attention has been given to the level of physical obtrusiveness of the used measures. We therefore explore the use of color-, and infrared-spectrum cameras for remote photoplethysmography (rPPG) as physically unobtrusive measures. Sixteen expert train traffic operators participated in a railway human-in-the-loop simulator. We used two machine learning models (AdaBoost and Random Forests) to predict low-, medium- and high-mental workload levels based on heart rate features in a leave-one-out cross-validated design. Results show above chance classification for low- and high-mental workload states. Based on infrared-spectrum rPPG derived features, the AdaBoost machine learning model yielded the highest classification performance.Organisation and Governanc