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

Sriram Sattiraju, Vaibhav Gollapalli, Aryan Shah, Timothy McMahan

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 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.

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

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 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.

Paper Structure

This paper contains 12 sections, 4 equations, 5 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED PRIOR RESEARCH
  3. METHODOLOGY
  4. RESULTS
  5. GAN Training Stability
  6. Feature-Level Distribution Alignment
  7. Schrödinger Bridge Energy on Real EEG
  8. Schrödinger Bridge Energy on GAN-Generated EEG
  9. Group-Level Comparison of Real and GAN EEG Transport Costs
  10. Preservation of Participant-Level Transport Trends
  11. NEUROADAPTIVE SYSTEM INTEGRATION
  12. CONCLUSION

Figures (5)

  • Figure D1: GAN training stability showing convergence of distribution alignment and bounded gradient penalty.
  • Figure D2: Comparison of real and GAN-generated EEG feature distributions across Theta, Alpha, and Engagement bands, showing strong alignment in distribution shape and variance.
  • Figure D3: Group-level comparison of SBP transport energy (mean ± std) for real and GAN EEG.
  • Figure D4: Participant-level SBP transport energy trends for real and GAN EEG across task transitions.
  • Figure E1: Closed-loop neuroadaptive system framework using EEG-derived cognitive energy as a control signal for real-time human–machine adaptation.