In neurorehabilitation systems, early detection of gait intention with a lower false-positive rate and higher sensitivity is a critical factor which dictates the successful operation of the exoskeleton or assistive system. Traditional supervised learning algorithms often fail to reach these goals due to the lack of ability to adapt to the diverse interaction between the external environment and the human user. A threshold regulation approach might be a simple yet significant addition to increasing the adaptability of the intention detection system. In this paper, the performance of a pseudo-online BCI system in asynchronous detection of human gait intention from movement-related Electroencephalography signals is investigated. Seven healthy individuals participated in the study who performed self-paced cycles of gait initiation and termination in multiple trials. A custom-made eight-channel EEG system was used to capture the movement-related neural signals while a pair of in-sole pressure sensors and an Electromyography (EMG) sensor were used for time locking the actual moments of movement onset and termination. A wavelet transform based method along with Hjorth parameters, was employed to extract informative features which were then used to train an SVM-RBF classifier with threshold regulation for asynchronous detection of gait intention. A high true positive rate, low false-positive rate, and very low latency were achieved by the proposed methodology. The results demonstrate the feasibility of the proposed framework in building a human-in-the-loop neurorehabilitation system.