Brain-machine interfaces (BMIs) have been used to incorporate the user intention to trigger robotic devices by decoding movement onset from electroencephalography (EEG). Active neural participation is crucial to promote brain plasticity thus to enhance the opportunity of motor recovery. This study presents the decoding of lower-limb movement-related cortical potentials (MRCPs) with continuous classification and asynchronous detection.We executed experiments in a customized gait trainer where 10 healthy subjects performed self-initiated ankle plantar flexion. We further analyzed the features, evaluated the impact of the limb side, and compared the proposed framework with other typical decoding methods. No significant differences were observed between left and right legs in terms of neural signatures of movement and classification performance. We obtained higher true positive rate (TPR), lower false positives, and comparable latencies with respect to the existing online detection methods. This paper demonstrates the feasibility of the proposed framework to build a closed-loop gait trainer. Potential applications include gait training neurorehabilitation in clinical trials.