Augmented State-Space Modeling and Control of Latent Arousal States Under Inhibitory and Excitatory Conditions

Goal:In modern high-stress environments, effectively regulating cognitive arousal, through enhancement to boost engagement or inhibition to manage excessive stress, is essential for maintaining mental well-being and optimizing human performance. Hence, this study extends existing state-space models by integrating time-varying parameters and disturbance inputs for enhanced representation of arousal dynamics inferred from skin conductance.Methods:We augmented nominal models with time-varying parameters, then developed a recursive Bayesian estimator for state tracking. Simulation-based validation was performed using skin conductance data from six participants, drawn from an experimental dataset of noninvasive wrist-worn physiological recordings acquired during cognitive stress and relaxation tasks. Adaptive and robust control architectures were designed for closed-loop regulation of latent arousal states.Results:Simulations based on experimental data showed that both controllers outperformed static methods. On average, under inhibitory and excitatory conditions, the adaptive controller achieved average RMSE reductions of 26.9% and 51.6%, respectively, while the robust controller achieved reductions of 16.0% and 23.4%. In complex multi-step tracking, the adaptive controller reduced average RMSE by 33.7% and control effort by 18.5%; similarly, the robust controller reduced RMSE by 32.6% and control effort by 15.1%.Conclusion:These findings demonstrate that adaptive and robust control strategies can reliably manage dynamic arousal regulation, offering potential for real-world neuroadaptive systems supporting human performance and well-being.