Closed-Loop Cognitive Stress Regulation Using Fuzzy Control in Wearable-Machine Interface Architectures

Keeping cognitive stress at a healthy range can improve the overall quality of life: helping subjects to decrease their high levels of arousal, which will make them relaxed, and elevate their low levels of arousal, which could increase their engagement. With recent advances in wearable technologies, collected skin conductance data provides us with valuable information regarding ones' cognitive stress-related state. In this research, we aim to create a simulation environment to control a cognitive stress-related state in a closed-loop manner. Toward this goal, by analyzing the collected skin conductance data from different subjects, we model skin conductance response events as a function of simulated environmental stimuli associated with cognitive stress and relaxation. Then, we estimate the hidden stress-related state by employing Bayesian filtering. Finally, we design a fuzzy control structure to close the loop in the simulation environment. Particularly, we design two classes of controllers: (1) an inhibitory controller for reducing cognitive stress and (2) an excitatory controller for increasing cognitive stress. We extend our previous work by implementing the proposed approach on multiple subjects' profiles. Final results confirm that our simulated skin conductance responses are in agreement with experimental data. In a simulation study based on experimental data, we illustrate the feasibility of designing both excitatory and inhibitory closed-loop wearable-machine interface architectures to regulate the estimated cognitive stress state. Due to the increased ubiquity of wearable devices capable of measuring cognitive stress-related variables, the proposed architecture is an initial step to treating cognitive disorders using non-invasive brain state decoding.