[2604.01653] Cognitive Energy Modeling for Neuroadaptive Human-Machine Systems using EEG and WGAN-GP

Computer Science > Machine Learning arXiv:2604.01653 (cs) [Submitted on 2 Apr 2026] Title:Cognitive Energy Modeling for Neuroadaptive Human-Machine Systems using EEG and WGAN-GP Authors:Sriram Sattiraju, Vaibhav Gollapalli, Aryan Shah, Timothy McMahan View a PDF of the paper titled Cognitive Energy Modeling for Neuroadaptive Human-Machine Systems using EEG and WGAN-GP, by Sriram Sattiraju and 3 other authors View PDF HTML (experimental) Abstract: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 analy...