An EEG-EMG-Based Motor Intention Recognition for Walking Assistive Exoskeletons

Abstract

Lower Limb Exoskeleton (LLE) has received considerable interests in strength augmentation, rehabilitation and walking assistance scenarios. For walking assistance, the LLE is expected to have the capability of recognizing the motor intention accurately. However, the methods for recognizing motor intention base on ElectroEncephaloGraphy (EEG) can not be directly used for recognizing the motor intention of human lower limbs, because it is difficult to distinguish left and right limbs. This paper proposes a human-exoskeleton interaction method based on EEG and ElectroMyoGrams (EMG)-Hierarchical Recognition for Motor Intention (HRMI). In which, the motor intention can be recognized by the EEG signal, and supplemented by EMG signals reflecting motor intention, the exoskeleton can distinguish the left and right limbs. An experimental platform is established to explore the performance of the proposed method in real life scenario. Ten healthy participants were recruited to perform a series of motions such standing, sitting, walking, and going up and down stairs. The results shown that the proposed method is successfully applied in real life scenarios and the recognition accuracy of standing and sitting than others.