An EEG-based non-linear state-space model for trust inference in a space-relevant human-autonomy teaming task.

Effective collaboration in human-autonomy teaming (HAT) depends on maintaining appropriately calibrated trust in the autonomous partner. Trust evolves as operators observe system behavior and task outcomes, requiring continuous monitoring rather than overall surveys or post-task self-reports. Electroencephalogram (EEG) offers high temporal resolution for tracking cognitive states; however, most existing EEG-based trust models are static or rely on linear mappings that fail to capture the non-linear and time-dependent nature of trust dynamics. This paper presents an EEG-based non-linear state-space model for continuous trust inference during a space-relevant supervisory task. The model integrates a low-dimensional linear latent process to represent the temporal evolution of trust with a non-linear manifold that maps EEG spectral features to the latent state, enabling trust prediction from both latent dynamics and neural representations. Model performance was evaluated using EEG data collected from participants monitoring autonomous systems with varying reliability and transparency, and compared against a linear dynamic model and a static model. After correcting the unit of statistical inference to participant-level averages, the proposed model achieved significantly higher correlation with self-reported trust than both the linear dynamic model (