LAUSR

More natural and intuitive control is expected to maximize the auxiliary effect of the powered prosthetic leg for lower limb amputees. In order to realize the stable and flexible walking of prosthetic legs in different terrains according to human intention, a brain–computer interface (BCI) based on motor imagery (MI) is developed. For the raw electroencephalogram (EEG) signals, discrete wavelet transform (DWT) is utilized to extract the time–frequency domain features, which are used as the input signals of the common spatial pattern (CSP) to obtain the time–frequency–space domain features of EEG signals. Then, a support vector machine (SVM) classifier and a directed acyclic graph (DAG) structure are combined to classify multiclass imaginary tasks. According to the result of human intention recognition, the prosthetic leg performs the corresponding gait trajectory generated by coding the ground reaction force (GRF). In addition, a sensory feedback loop is established by functional electrical stimulation (FES), which feeds back the movement of the prosthetic leg to human in real time. The effectiveness and feasibility of the developed EEG-based volitional control of powered prosthetic legs have been validated by three subjects, all of whom were able to fulfill smoothly walking on the floor, ascending stairs, and descending stairs according to their own intentions using prosthetic legs. Note to Practitioners—This article was inspired by the problem that the control mode of the prosthetic leg walking is not natural and intuitive, but it can also be applicable to other powered prosthetic limbs. Existing methods of controlling the walking patterns of the prosthetic leg usually require explicit human manipulation and are not convenient to use. In this article, a novel strategy was proposed, that is, using the human mind to choose the ambulation mode of prosthetic legs. This article described the methods of human brain activity information acquisition and intention recognition. Then, gaits inspired by the healthy leg are designed for the prosthetic leg in three terrains, including walking on the floor, ascending stairs, and descending stairs. In addition, this article provided a way for human to perceive the movement of prosthetic legs, which can make human control the prosthetic leg more smoothly. Experiments on healthy subjects have shown that this approach is feasible. However, no experiments have been conducted on lower limb amputees, and adding more walking patterns may reduce the accuracy of human intention recognition. In the future work, we will study how to accurately identify more categories of human intentions and further improve the control strategy so as to enhance the auxiliary effect of prosthetic legs on amputees.