Estimating Motor Control Difficulty in Human–Robot Fine Co-Manipulation Tasks Using Brain Activities

Abstract

Improper controller parameter settings in physical human–robot interaction (pHRI) can lead to instability, compromising both safety and system performance. This study investigates the relationship between cognitive and physical aspects of co-manipulation by leveraging electroencephalography (EEG) to predict instability in physical human–robot interaction. Using elastic net regression and deep convolutional neural networks, we estimate instability as subjects guide a robot through predefined trajectories under varying admittance control settings. Our results show that EEG signals can predict instability up to 2 s before it manifests in force data. Moreover, the deep learning-based approach significantly outperforms elastic regression, achieving a notable (∼10%) improvement in predicting the instability index. These findings highlight the potential of EEG-based monitoring for enhancing real-time stability assessment in pHRI.