Cognitive Energy Modeling for Neuroadaptive Human-Machine Systems using EEG and WGAN-GP
This work enables data-efficient neuroadaptive systems by validating synthetic EEG for real-time cognitive energy modeling, which can be used to adjust human-machine interactions based on user states.
The paper tackled the challenge of modeling cognitive state transitions and energy costs from EEG data by using the Schrödinger Bridge Problem (SBP) to evaluate if GAN-generated synthetic EEG preserves the necessary distributional geometry, demonstrating strong agreement in transition energies between real and synthetic data from Stroop tasks.
Electroencephalography (EEG) provides a non-invasive insight into the brain's cognitive and emotional dynamics. However, modeling how these states evolve in real time and quantifying the energy required for such transitions remains a major challenge. The Schrödinger Bridge Problem (SBP) offers a principled probabilistic framework to model the most efficient evolution between the brain states, interpreted as a measure of cognitive energy cost. While generative models such as GANs have been widely used to augment EEG data, it remains unclear whether synthetic EEG preserves the underlying dynamical structure required for transition-based analysis. In this work, we address this gap by using SBP-derived transport cost as a metric to evaluate whether GAN-generated EEG retains the distributional geometry necessary for energy-based modeling of cognitive state transitions. We compare transition energies derived from real and synthetic EEG collected during Stroop tasks and demonstrate strong agreement across group and participant-level analyses. These results indicate that synthetic EEG preserves the transition structure required for SBP-based modeling, enabling its use in data-efficient neuroadaptive systems. We further present a framework in which SBP-derived cognitive energy serves as a control signal for adaptive human-machine systems, supporting real-time adjustment of system behavior in response to user cognitive and affective state.