Visually Guided Walking in Complex Environments: Neural, Biomechanical, and Translational Insights for Neuroprosthetics

Walking is a deceptively complex behavior. Each step depends on the continuous integration of visual input, vestibular and proprioceptive signals, and motor commands distributed across cortical, subcortical, and spinal circuits. Vision plays a particularly central role: it anticipates obstacles, calibrates foot placement, and tunes locomotor control before the foot contacts the ground. When this visuomotor loop is disrupted by injury, neurological disease, aging, limb loss, or the use of a prosthetic or exoskeletal device, gait becomes slower, more variable, and metabolically costly. Mobile brain/body imaging (MoBI) including high-density EEG, fNIRS, EMG, inertial sensors, and motion capture, enables in-situ measurement of the brain and body during complex realistic behaviours. These advances have direct implications for neuroprosthetics: powered prostheses, exoskeletons, and brain–machine interfaces must ultimately operate inside the user’s own visuomotor control loop.

This Research Topic aims to bring together experimental, computational, and translational work that uses laboratory-based, real world, or virtual-reality (VR) paradigms to better understand how the human nervous system uses vision to plan, execute, and adapt walking, and how those insights can be transferred to the design, control, and evaluation of neuroprostheses and assistive devices. We welcome studies spanning healthy adults, clinical populations, and operational end-users, and methods ranging from MoBI and electrophysiology to musculoskeletal modeling, machine learning, and human–machine interface design.

We welcome Original Research, Reviews, Mini-Reviews, Methods, Brief Reports, and Perspectives covering, but not limited to:
• Cortical dynamics of visually guided locomotion: EEG/fNIRS signatures of obstacle negotiation, visual flow manipulation, terrain transitions, and gaze–step coupling during overground and treadmill walking.
• Visuomotor adaptation in VR and AR: sensory reweighting, recalibration, and transfer of learning between virtual and real environments.
• Neuroprosthetic and exoskeleton control: visually informed intent decoding, shared control, and adaptive assistance strategies for lower-limb devices.
• Mobile brain/body imaging (MoBI) methods: artifact removal, source localization, multimodal sensor fusion, and validation frameworks for ambulatory recordings.
• Cognitive–motor interactions: dual-task paradigms, attentional load, and decision-making during locomotion in immersive environments.
• Clinical and translational applications: VR-based gait rehabilitation after stroke, spinal cord injury, lower-limb amputation, Parkinson’s disease, and aging-related mobility decline.
• Human performance and operational contexts: visually demanding locomotion in extreme, low-gravity, underwater, or military-relevant scenarios.
• Open data, benchmarks, and reproducibility: shared datasets, analysis pipelines, and standardized VR locomotion paradigms to accelerate cross-lab comparison.

Together, contributions will advance a mechanistic understanding of how vision shapes walking and inform the next generation of neuroprosthetic technologies that work with, not against, the user’s natural visuomotor control.