This project aims to incorporate a novel wearable neural interface based on real-time EMG decomposition onto the exoskeleton H3 for an accurate real-time motion prediction and control. The developed wearable interface incorporates a novel high-performance surface EMG recording system (Myolink) which allows up to 128 channels of surface EMG recording at a sampling frequency of 2000 Hz and exhibits <0.4μVrms input-referred-noise; which outperforms most of the existing portable interfaces. The real-time EMG decomposition system, on the other hand, utilises the novel computational methods of blind source separation to identify the neural information sent from the spinal cord to human muscles. By mapping the decoded motor neuron activity onto the respective degree of freedoms, this project will demonstrate for the first-time assistive locomotion based on a real-time spinal neural interface. This project will harvest the valuable experimental scenarios offered by EUROBENCH to systematically examine the robustness of both the developed interface and the proposed control. The acquired experimental results can benchmark the design specification of portable neural interfaces during normal and high dynamical conditions. Additionally, the acquired results may also define the feasibility of using decoded motor neuron commands for real-time control, and benefits the community of wearable robotics.