Brain-Machine Interface (BMI) has emerged as a powerful tool for assisting disabled
people and for augmentinghuman 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 on-line estimation, a time lag is introduced,
and in particular, a linear model that correlates the EMG to the EEG signal is constructed
utilizing motion-related features extracted from multi-location EEG measurements. The
constructed model is used to estimate the human muscular activity of shoulder joint from
EEG. The proposed approach is experimentally verified. Our results suggest that the
estimation of EMG based on EEG is feasible, further demonstrating the potential of using
EEG signals via the control of brain-machine interface to support human activities
