Construction of Power Assistive System for the Control of Upper Limb Wearable Exoskeleton Robot with Electroencephalography Signals

Brain-Machine Interface (BMI) has emerged as a powerful tool for assisting disabled people and for augmenting human performance. In this work, we propose a motion estimation method based on electroencephalography (EEG) signals to realize the power assistance. In order to improve the accuracy of online estimation, time lag is introduced, and in particular, a linear model that correlates the EMG signal to the EEG signals is constructed based on motion-related features extracted from multi-location EEG signal measurements. The constructed model is used to estimate the human muscular activity of shoulder joint from EEG signals. The proposed approach is experimentally verified. Our results suggest that the estimation of EMG signal based on EEG signals is feasible, and demonstrate the potential of using EEG signals via the control of brain-machine interface to support human activities.