TrustNet: Deep Ensemble Learning for EEG-Based Trust Recognition in Human–Robot Cooperation

摘要

Recognizing human trust states is crucial for effective human–robot cooperation, as it enables robots to evaluate their decisions and align their actions with human expectations and preferences. However, the complex and dynamic nature of trust in these interactions has led to a lack of effective real-time, generalized trust recognition methods. Electroencephalography (EEG) offers a promising solution by exploring the relationship between trust and specific brain activity patterns. Nonetheless, individual variability in EEG data presents challenges in achieving consistently high recognition performance. In this article, we propose a systematic approach to recognize trust in human–robot cooperation based on EEG data. To address individual differences and achieve robust performance, we introduce TrustNet, a novel stacking ensemble learning model that combines the strengths of several heterogeneous deep learning architectures. Validated on our constructed dataset, EEGTrust, our method achieves an average accuracy of 91.31% in slicewise experiments and 66.11% in trialwise experiments, significantly outperforming baseline methods. Furthermore, we investigate the significance of EEG channels and frequency bands for trust recognition. Results highlight the importance of gamma and beta frequency bands, along with electrodes positioned over frontal and parietal scalp regions, with stronger effects observed at right-hemisphere electrode locations. Feature reduction analysis identified an optimal EEG configuration comprising gamma, beta, and alpha frequency bands from electrodes over nine key scalp regions, specifically excluding middle and left occipital electrode groups.