Faghih, Rose T.2022-06-18August 2022021-08August 202https://hdl.handle.net/10657/9348Emotions exert powerful influences on all aspects of cognition. In terms of emotional valence, negative emotions can hinder our ability to concentrate and recall, while long periods of overly positive emotions can take a toll in rational decision-making. In addition, lower levels of emotional arousal impair our motivation and productivity, while high levels of this cognitive stress greatly impacts quality of life and life expectancy. Our goal here is to investigate the closed-loop control of emotional levels, as this could improve current and future medical and psychiatric diagnoses and treatments. Open-loop methods are employed in traditional neurostimulation approaches, in which there is no feedback of the internal brain state, impeding the controller of automatically adjusting to neurophysiological changes. To address this issue, we use physiological measurements attained with wearable devices to infer hidden brain states. Specifically, we use electrodermal activity and facial electromyogram as feedback biomarkers for arousal and valence levels, respectively. Using a systematic approach to estimate and track hidden emotional levels, we develop and employ a simulation environment in which we recreate states of low or high valence as well as low or high arousal within the brain model. From the simulated physiological responses, we extract binary and continuous features before using a Bayesian filter to estimate brain states in real-time. We close the loop with a fuzzy logic controller (FLC) optimized with a genetic algorithm. FLCs have been widely used because of how well they mitigate the inaccuracies and uncertainties of the real world. However, manually tuning the parameters can be time-consuming. To this end, we employ a genetic algorithm to optimize the parameters and adapt the controller to different subjects. Moreover, we discuss the investigation of non-invasive stimuli in cognitive processes. Specifically, the use of deep-learning music generation as an interesting type of brain stimulation for closed-loop control therapies. Final results illustrate the feasibility of our approach in recovering, tracking and regulating the hidden emotional states using physiological measurements obtained with wearable devices. Ultimately, this study suggests initial prospects of regulating mental states with data collected non-invasively, offering potential towards novel treatments to various mental illnesses.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Closed-loopControlBrainStatesState-spaceFuzzyGenetic algorithmWearable devicesSkin conductanceFacial electromyography2022-06-18Thesisborn digital