Adaptive digital twin technology for real-time emotional and cognitive state decoding

We are developing a new class of neuroscience and human-state awareness technology based on Adaptive Digital Twins (ADT) for real-time decoding of cognitive and emotional responses from EEG and multimodal biosignals. Unlike conventional black-box AI approaches, this framework combines dynamical systems theory, modal analysis, adaptive estimation, and control-oriented system identification to generate interpretable models of evolving brain dynamics. The technology treats the brain as a high-dimensional dynamical system whose dominant cognitive modes can be identified and tracked in real time. Current work focuses on extracting dynamical signatures associated with emotional response, cognitive workload, operator attention, fatigue, stress, and human decision-making. Initial proof-of-concept studies have demonstrated the ability to identify dominant dynamical modes from EEG data using Operational Modal Analysis (OMA) and Adaptive Digital Twin methodologies. Current development includes validation using public EEG datasets and experimental neuroscience data, with emphasis on interpretable cognitive-state mapping and adaptive estimation. Potential applications include emotion-aware AI systems, human-autonomy teaming, adaptive driver/operator monitoring, neuroadaptive interfaces, aerospace human factors, digital health, intelligent robotics, and neurological assessment. We are seeking sponsored research partnerships and collaborators interested in next-generation neurotechnology, emotionally aware intelligent systems, and real-time human-state awareness technologies. Current interests include multimodal sensing integration, real-time implementation, and application demonstrations in transportation, robotics, aerospace, defense, and healthcare systems.